2008.01.30 Selecting gamma-jet candidates out of the jet trees

Ilya Selyuzhenkov January 30, 2008

Data set

jet trees by Murad Sarsour for pp2006 run, runId=7136022 (~60K events, no triggerId cuts yet)

Jets gross features

• Figure 1: Distribution of number of jets per event. Same data on a log scale is here.

  

• Figure 2: Distribution of electromagnetic energy (EM) fraction, R_EM, for di-jet events (number of jets/event = 2).
  R_EM = [E_t(endcap)+E_t(barrel)]/E_t(total).
  Black histogram is for R_EM1 = max(Ra, Rb), red is for R_EM2 = min(Ra, Rb).
  Ra and Rb are EM fraction for jets in the di-jet event.
  Same data on a log scale is here.

   

  

Gamma-jet isolation cuts list:

1. selecting di-jet events with one of the jet dominated by EM energy,
   and another one with more hadronic energy:

   R_EM1 >0.9 and R_EM2 < 0.9

2. selecting di-jet events with jets pointing opposite in azimuth:

   cos(phi1 - phi2) < -0.9

3. requiring the number of associated charged tracks with a first jet (with maximum EM fraction) to be less than 2:

   nChargeTracks1 < 2

4. requiring the number of fired EEMC towers associated with a first jet (with maximum EM fraction) to be 1 or 2:

   0 < nEEMCtowers1 < 3

    

Applying gamma-jet isolation cuts

• Figure 3: Distribution of eta vs number of EEMC towers for the first jet (with maximum EM fraction).
  Cuts:1-3 applied (no 0 < nEEMCtowers1 < 3 cut).

  

• Figure 4: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
  vs transverse momentum, pt2, of the second jet.
  Cuts:1-4 applied

  

• Figure 5: Distribution of mean transverse momentum, < pt1 >, of the first jet (with maximum EM fraction)
  vs transverse momentum, pt2, of the second jet.
  Cuts:1-4 applied

  

• Figure 6: Distribution of pseudorapidity, eta1, of the first jet (with maximum EM fraction)
  vs pseudorapidity, eta2, of the second jet.
  Cuts:1-4 applied

  

• Figure 7: Distribution of azimuthal angle, phi1, of the first jet (with maximum EM fraction)
  vs azimuthal angle, phi2, of the second jet.
  Cuts:1-4 applied

  

• Figure 8: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
  vs transverse energy sum for the EEMC towers associated with this jet.
  Cuts:1-4 applied

  

• Figure 9: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
  vs transverse momentum, pt2, of the second jet.
  Cuts:1-4 + Et(EEMC) > 3.0 GeV

  

 

2008.02.13 Gamma-jet candidates: EEMC response

Ilya Selyuzhenkov February 13, 2008

Data sample

Gamma-jet selection cuts are discussed here. There are 278 candidates found for runId=7136022.
Transverse momentum distribution for the gamma-jet candidates can be found here.

Vertex z distribution for di-jet and gamma-jet events

• Figure 1: Vertex z distribution.

  Red line presents gamma-jet candidates (scaled by x50). Black is for all di-jet events.
  Same data on a log scale is here.

  

• Figure 2: Average vertex z as a function of transverse momentum of the fist jet (with a largest EM energy fraction).
  Red is for gamma-jet candidates. Black is for all di-jet events.
  Strong deviation from zero for gamma-jet candidates at pt < 5GeV?

   

  

EEMC response for the gamma-jet candidate

EEMC response event by event for all 278 gamma-jet candidate can be found in this pdf file.
Each page shows SMD/Tower energy distribution for a given event:

1. First row on each page shows SMD response
   for the sector which has a maximum energy deposited in the EEMC Tower
   (u-plane is on the left, v-plane is on the right).

2. In the left plot (u-plane energy distribution) numerical values for
   pt of the first jet (with maximum EM fraction) and the second jet are given.

3. In addition, fit results assuming gamma (single Gaussian, red line) or
   neutral pion (double Gaussian, blue line ~ red+green) hypotheses are given.

4. m_{gamma gamma} value (it is shown in the right plot for v-plane).

   If m_{gamma gamma} value is negative, then the reconstruction procedure has failed
   (for example, no uv-strips intersection found, or tower energy and uv-strips intersection point mismatch, etc).
   EEMC response for these "bad" events can be found in this pdf file.

   If reconstruction procedure succeded, then
   m_{gamma gamma} gives reconstructed invariant mass assuming that two gammas hit the calorimeter.

   Figure 3: invariant mass distribution (assuming pi0 hypothesis).

   Note, that I'm still working on my fitting algorithm (which is not explained here),
   and fit results and the invariant mass distribution will be updated.

   

    

5. It is also shown the ratio for each u/v plane
   of the integrated single Gaussian fit (red line) to the total energy in the plane
   (look for "gamma U/V " values on the right v-plane plot).

6. Second and third rows on each page show the energy deposition in the
   tower, pre-shower1, pre-shower2, and post-shower as a function of eta:phi (etaBin:phiBin).

7. Last row shows the hit distribution in the SMD for all sectors
   (u-plane on the left, v-plane of the right).

Playing with a different cuts

Trying to isolate the real gammas which hits the calorimeter,
I have sorted events into different subsets based on the following set of cuts:

1. EEMC gamma-jet cuts (energetic photon hits EEMC with pt similar or greater to that of the opposite jet)

   if (invMass < 0) reject
   if (jet2_pt > jet1_pt) reject
   if (jet1_pt < 7) reject
   if (minFraction < 0.75) reject
   (minFraction = gamma U/V - is a fraction of the integrated single Gaussian peak to the total energy in the uv-plane)

   Figure 4: Sample gamma-jet candidate EEMC response
   (all gamma-jet candidates selected according to these conditions can be found in this pdf file):

   

2. EEMC pi0 cuts:

   if (invMass < 0) reject
   if (jet2_pt < jet1_pt) reject
   if (jet2_pt < 7) reject
   if (minFraction < 0.75) reject

   Event by event EEMC response for pi0 (di-jet) candidates
   selected according to these conditions can be found in this pdf file.

 

2008.02.20 Gamma-jet candidates: more statistics from jet-trees

Ilya Selyuzhenkov February 20, 2008

Short summary

After processing all available jet-trees for pp2006 (ppProductionLong),
and applying all "gamma-jet" cuts (which are described below):

• there are 47K di-jet events selected

• for pt1>7GeV there are 5,4K gamma-jet candidates (3,7K with an additional cut of pt1>pt2)

• Figure 1: 2,4K events with both pt1, pt2 > 7GeV

  

• 721 candidates within a range of pt1>pt2 and both pt1, pt2 > 7 GeV

Data set

jet trees by Murad Sarsour for pp2006 run, number of runs processed: 323
4.7M di-jet events found (no triggerId cuts yet)

Di-jets gross features

• Figure 2: Distribution of electromagnetic energy (EM) fraction, R_EM, for di-jet events (number of jets/event = 2).
  R_EM = [E_t(endcap)+E_t(barrel)]/E_t(total).
  Black histogram is for R_EM1 = max(Ra, Rb), red is for R_EM2 = min(Ra, Rb).
  Ra and Rb are EM fraction for jets in the di-jet event.
  Same data on a log scale is here.

   

  

Gamma-jet isolation cuts:

1. selecting di-jet events with one of the jet dominated by EM energy,
   and another one with more hadronic energy:

   R_EM1 >0.9 and R_EM2 < 0.9

2. selecting di-jet events with jets pointing opposite in azimuth:

   cos(phi1 - phi2) < -0.9

3. requiring the number of associated charged tracks with a first jet (with maximum EM fraction) to be less than 2:

   nChargeTracks1 < 2

4. requiring the number of fired EEMC towers associated with a first jet (with maximum EM fraction) to be 1 or 2:

   0 < nEEMCtowers1 < 3

    

Applying gamma-jet isolation cuts

• Figure 3: Distribution of eta vs number of EEMC towers for the first jet (with maximum EM fraction).
  Cuts:1-3 applied (no 0 < nEEMCtowers1 < 3 cut).

  

• Figure 4: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
  vs transverse momentum, pt2, of the second jet.
  Cuts:1-4 applied

  

• Figure 5: Distribution of mean transverse momentum, < pt1 >, of the first jet (with maximum EM fraction)
  vs transverse momentum, pt2, of the second jet.
  Cuts:1-4 applied

  

• Figure 6: Distribution of pseudorapidity, eta1, of the first jet (with maximum EM fraction)
  vs pseudorapidity, eta2, of the second jet.
  Cuts:1-4 applied

  

• Figure 7: Distribution of azimuthal angle, phi1, of the first jet (with maximum EM fraction)
  vs azimuthal angle, phi2, of the second jet.
  Cuts:1-4 applied

  

• Figure 8: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
  vs transverse energy sum for the EEMC towers associated with this jet.
  Cuts:1-4 applied

  

• Figure 9: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
  vs transverse momentum, pt2, of the second jet.
  Cuts:1-4 + Et(EEMC) > 3.0 GeV

  

 

2008.02.27 Tower based clustering algorithm, and EEMC/BEMC candidates

Ilya Selyuzhenkov February 27, 2008

Gamma-jet candidates before applying clustering algorithm

Gamma-jet isolation cuts:

1. selecting di-jet events with the first jet dominated by EM energy,
   and the second one with a large fraction of hadronic energy:

   R_EM1 >0.9 and R_EM2 < 0.9

2. selecting di-jet events with jets pointing opposite in azimuth:

   cos(phi1 - phi2) < -0.8

3. requiring no charge tracks associated with a first jet (jet with a maximum EM fraction):

   nCharge1 = 0

Figure 1: Transverse momentum

Figure 2: Pseudorapidity

Figure 3: Azimuthal angle

Tower based clustering algorithm

• for each gamma-jet candidate finding a tower with a maximum energy
  associated with a jet1 (jet with a maximum EM fraction).

• Calculating energy of the cluster by finding all adjacent towers and adding their energy together.

• Implementing a cut based on cluster energy fraction, R_cluster, where

  R_cluster is defined as a ratio of the cluster energy
  to the total energy in the calorimeter associated with a jet1.
  Note, that with a cut Ncharge1 =0, energy in the calorimeter is equal to the jet energy.

 

Distribution of cluster energy vs number of towers fired in EEMC/BEMC

Figure 4: R_cluster vs number of towers fired in EEMC (left) and BEMC (right). No pt cuts.

Figure 5: R_cluster vs number of towers fired in EEMC (left) and BEMC (right). Additional cut: pt1>7GeV

Figure 6: jet1 pseudorapidity vs number of towers fired in EEMC (left) and BEMC (right).

 

R_cluster>0.9 cut: EEMC vs BEMC gamma-jet candidates

EEMC candidates: nTowerFiredBEMC=0
BEMC candidates: nTowerFiredEEMC=0

Figure 7: Pseudorapidity (left EEMC, right BEMC candidates)

Figure 8: Azimuthal angle (left EEMC, right BEMC candidates)

Figure 9: Transverse momentum (left EEMC, right BEMC candidates)

 

Number of gamma-jet candidates with an addition pt cuts

Figure 10: Transverse momentum (left EEMC, right BEMC candidates): pt1>7GeV

Figure 11: Transverse momentum (left EEMC, right BEMC candidates): pt1>7 and pt2>7

2008.03.03 EEMC SMD: u/v-strip energy distribution

Ilya Selyuzhenkov March 03, 2008

Data set: ppLongitudinal, runId = 7136033.

Some observations/questions:

1. In general distributions look clean and good

2. Sectors 7 and 9 for v-plane and sector 7 for u-plane are noise.

3. Sector 9 has a hot strip (id ~ 120)

4. In sector 3, strips id=0-5 in v-plane are hot (see figure 2 right, bottom)

5. Sectors 2 and 8 in u-plane and sectors 3 and 9 in v-plane have missing strips id=283-288?

6. Strips 288 are always empty?

Figure 1:Average energy E in the strip vs sector and strip number (max < E > = 0.0027)
same figure on a log scale

Figure 2: Average energy E for E>0.02 (max < E > = 0.0682)
Same figure on a log scale

2008.03.12 Gamma-jet candidates: 2-gammas invariant mass and Eemc response

Ilya Selyuzhenkov March 12, 2008

Gamma-jet candidates: 2-gammas invariant

Note: Di-jet transverse momentum distribution for these candidates can be found on figure 11 at this page

Figure 1:Invariant mass distribution for gamma-jet candidates assuming pi0 (2-gammas) hypothesys

Figure 2:Invariant mass distribution for gamma-jet candidates assuming pi0 (2-gammas) hypothesys
with an additional SMD isolation cut: gammaFraction >0.75
GammaFraction is defined as ratio of the integral
other SMD strips for the first peak to the total energy in the sector

 

EEMC response for the gamma-jet candidates (gammaFraction >0.75)

1. pdf file (first 100 events) with event by event EEMC response for the candidates reconstructed into pion mass (gammaFraction >0.75)

2. pdf file with event by event EEMC response for the candidates not reconstructed into pion mass
   (second peak not found), but has a first peak with gammaFraction >0.75.

 

2008.03.20 Sided residual and chi2 distribution for gamma-jet candidates

Ilya Selyuzhenkov March 20, 2008

Side residual (no pt cut on gamma jet-candidates)

The procedure to discriminate gamma candidate from pions (and other background)
based on the SMD response is described at Pibero's web page.

 

Figure 1: Fit integral vs maximum residual for gamma-jet candidates requesting
no energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).

Black line is defined from MC simulations (see Jason's simulation web page, or Pibero's page above).

 

Figure 2: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
no energy deposited in pre-shower 1 cluster and
no energy deposited in post-shower cluster (this cut is not really essential in demonstrating the main idea)

 

Figure 3: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

 

Side residual: first and second jet pt are greater than 7GeV

Event by event EEMC response for gamma-jet candidates for the case of
no energy deposited in the EEMC pre-shower 1 and 2 can be found in this pdf file

 

Figure 4: Fit integral vs maximum residual for gamma-jet candidates requesting
no energy deposited in the EEMC pre-shower 1 and 2

 

Figure 5: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
no energy deposited in pre-shower 1 cluster and
no energy deposited in post-shower cluster

 

Figure 6: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

 

Chi2 distribution for gamma-jet candidates

Monte Carlo shape

Event Monte Carlo shape allows to distinguish gammas from background by cutting at chi2/ndf < 0.5
(although the distribution looks wider than for the case of Will's shape).

 

Figure 7: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
no energy deposited in both clusters of pre-shower 1 and 2

 

Figure 8: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

 

Will''s shape

Less clear where to cut on chi2?

 

Figure 9: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
no energy deposited in both clusters of pre-shower 1 and 2

 

Figure 10: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2 

 

2008.03.26 Sided residual and chi2 distribution for gamma-jet candidates (pre1,2 sorted)

Ilya Selyuzhenkov March 26, 2008

gamma-jet candidates (no pt cut)

Definitions:

• F_peak - integral for a fit within [-2,2] strips around SMD u/v peak
• D_peak - integral over the data within [-2,2] strips around SMD u/v peak
• D_tail^max (D_tail^min) - maximum (minimum) integral over the data tail within +-[3,30] strips from a SMD u/v peak
• F_tail is the integral over the fit tail within [3,30] strips from a SMD u/v peak.
• Maximum residual = D_tail^max - F_tail

All results are for combined distributions from u and v planes: ([u]+[v])/2
Gamma-jet isolation cuts described here
Additional quality cuts:

1. Matching between 3x3 tower cluster and u-v high strip intersection
2. At least 4 strips fired within [-2,2] strips from a peak

Figure 1: F_peak vs maximum residual
for various cuts on energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).

 

Figure 2: F_data vs D_tail^max
Note:This plot is fit independend (only the peak position is defined based on the fit)

 

Figure 3: F_data vs D_tail^max-D_tail^max

 

Figure 4: Gamma transverse momentum vs jet transverse momentum

 

gamma-jet candidates: pt > 7GeV

Figure 5: F_peak vs maximum residual
for various cuts on energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).

Figure 6: F_data vs D_tail^max
Note:This plot is fit independend (only the peak position is defined based on the fit)

Figure 7: F_data vs D_tail^max-D_tail^max

Figure 8: Gamma transverse momentum vs jet transverse momentum

 

gamma-jet candidates: eta, phi, and max[u,v] strip distributions (no pt cuts)

Figure 9: Gamma pseudorapidity vs jet pseudorapidity

 

Figure 10: Gamma azimuthal angle vs jet azimuthal angle
Note: for the case of Pre1>1 && Pre2==0 there is an enhancement around phi_gamma = 0?

 

Figure 11: maximum strip in v-plane vs maximum strip in u-plane

 

Chi2 distribution for gamma-jet candidates (no pt cuts)

Figure 12:Chi2/ndf for gamma-jet candidates using Monte Carlo shape (combined for [u+v]/2 plane )

Figure 13:Chi2/ndf for gamma-jet candidates (combined for [u+v]/2 plane ) using Will's shape

 

2008.03.28 EEMC SMD shapes: gamma's from gamma-jets (data), MC, and eta-meson analysis

Ilya Selyuzhenkov March 28, 2008

Some observations:

1. SMD data-driven shapes from different analysis are in a good agreement (Figure 1, upper left plot)
2. Overall MC shape is too narrow compared to the data shapes (Figure 1, upper left plot)
3. Shapes are similar with or without gamma-jet 7GeV pt cut (compare Figures 1 and 2),
   what may indicate that shape is independent on energy (at least within our kinematic limits).
4. Data-driven and MC shapes are getting close to each other (Figure 4, upper left plot)
   when requiring no energy above threshold in both preshower layers and
   with suppressed contribution from pi0 background.
   The latter is achieved by using the information on
   reconstructed invariant mass of 2gamma candidates (compare Figure 3 and 4).

   One interpretation of this can be that in Monte Carlo simulations
   the contribution from the material in front of the detector is underestimated

5. Energy distribution for each strip in the SMD peak does not looks like a Gaussian (Figure 5),
   what makes very difficult to interpret results obtained from chi2 analysis (Figure 6-8).

6. Triple Gaussian fit gives a better description of the data shapes,
   compared to the double Gaussian function (compare red and black lines on Figure 1-4)

 

Figure 1: EEMC SMD shape comparison for various preshower cuts
(black points shows u-plane shape only, v-plane results can be found here)

 

Figure 2: EEMC SMD shape comparison for various preshower cuts with gamma-jet pt cut of 7GeV
(black points shows u-plane shape only, v-plane results can be found here)

 

Figure 3: Shapes with an additional cut on 2-gamma candidates within pi0 invariant mass range.
Sample invariant mass distribution using "simple" pi0 finder can be found here
(black points shows u-plane shape only, v-plane results can be found here)

 

Figure 4: Shapes for the candidates when "simple" pi0 finder failed to find a second peak
(black points shows u-plane shape only, v-plane results can be found here)

 

Figure 5: Strip by strip SMD energy distribution.
Only 12 strips from the right side of the maximum are shown.
Zero strip (first upper left plot) corresponds to the high strip in the shape
Note, that already at the 3rd strip from a peak,
RMS values are comparable to those for a mean, and for a higher strips numbers RMS starts to be bigger that mean.
(results for u-plane only, v-plane results can be found here)

 

Comparing chi2 distributions for gamma-jet candidates using MC, Will, and Pibero's shapes

Results for side residual (together with pt, eta, phi distributions) for gamma-jet candidates can be found at this web page

Red histograms on Figures 6-8 shows chi2 distribution from MC-photons (normalized at chi2=1.4)
Blue histograms on Figures 6-8 shows chi2 distribution from MC-pions (normalized at chi2=1.4)

Figure 6: Chi2/ndf for gamma-jet candidates using Monte Carlo shape

 

Figure 7: Chi2/ndf for gamma-jet candidates using Will's shape (derived from eta candidates based on Weihong's pi0-finder)

Figure 8: Chi2/ndf for gamma-jet candidates using Pibero's shape (derived from eta candidates)

 

2008.04.02 EEMC SMD shapes: data-driven (eta, gamma-jet) vs Monte Carlo (single gamma, gamma-jet)

Ilya Selyuzhenkov April 02, 2008

Some observations:

1. SMD data-driven shapes from eta-meson and gamma-jet studies
   are in a good agreement for different preshower conditions
   (compage Fig.1 green circles/triangles in upper-left/bottom-right plots)
2. single gamma MC shapes show preshower dependance,
   but they are still narrower compared to the data shapes
   (compare Fig.1 green circles vs blue open squares)
3. MC shapes for gamma-jet and single gamma are consistent (Fig.1, bottom right plot)

 

Figure 1: EEMC SMD shape comparison for various preshower cuts
Note:Only MC gamma-jet shape (open red squares) is the same on all plots

2008.04.02 Sided residual: Using data driven gamma-jet shape (3 gaussian fit)

Ilya Selyuzhenkov April 02, 2008

Figure 1: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
No EEMC SMD based cuts

 

Figure 2: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
"Simple" pi0 finder can not find a second peak

 

Figure 3: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
"Simple" pi0 finder reconstruct the invarian mass within [0.1,0.18] range

 

Figure 4: Side residual distribution (Projection for side residual in Figs.1-3 on vertical axis)

 

Figure 5: Signal (green: m < 0) vs background (black, red) separation

2008.04.02 Sided residual: single gamma Monte-Carlo simulations

Ilya Selyuzhenkov April 02, 2008

Side residual: single gamma Monte-Carlo simulations

Figure 1: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
No EEMC SMD based cuts

2008.04.03 chi2-shape subtraction for different Preshower conditions

Ilya Selyuzhenkov April 03, 2008

Request from Hal Spinka:

Hi Ilya,

I think you gave up on the chi-squared method too quickly, and am sorry I missed the phone meeting last week. So, I would like to make a request that will hopefully take a minimal amount of your time to show that all is okay. Then, if there is a delay in getting the sided residual information out and into the beam use request, you can still fall back on the chi-squared method.

In your March 28 posting, Figure 8 at the bottom, I would like to get numerical values for the events per bin for the black curves. I won't use the preshower1>0 and preshower2=0 data, so those you don't need to send. Also, I won't use the red or blue curve information.

I think your problem has been that you normalized your curves at chi-squared/ndf = 1.4 instead of the peak. What I plan to do is to normalize the (pre1=0, pre2=0) to the (pre1=0, pre2>0) data in the peak and subtract. The (pre1=0, pre2=0) set should have some single photons, but also some multiple photons. The (pre1=0, pre2>0) should also have single photons, and more multiple photons, since the chance that one of them will convert is larger. The difference should look roughly like your blue curve, though perhaps not exactly if Pibero's mean shower shape is not perfect (which it isn't). I will do the same thing with taking the difference between (pre1>0, pre2>0) and (pre1=0, pre2=0), and again the difference should look roughly like your blue curve. The (pre1>0, pre2>0) data should have even larger fraction of multiple photons than either of the other two data sets. I would expect the two difference curves to look approximately the same.

Hope this is possible for you to do. Since our reduced chi-squared curve looks so much like the one from CDF, I am pretty confident that we are okay, but this should be checked to convince people that we are not doing anything terribly wrong.

Reply by Ilya:

Dear Hal,

I have tried to implement your idea and produce a figure attached.

There are 4 plots in it:

1. Upper left plot shows normalized to unity (at maximum) chi2 distribution (obtained with Pibero shape for gamma-jet candidates) for a different pre1, pre2 conditions

2. Upper right plot shows bin-by-bin difference: a) between normalized chi2 for pre1=0, pre2>0 and pre1=0, pre2=0 (red) and b) between normalized chi2 for pre1>0, pre2>0 and pre1=0, pre2=0 (blue)

3. Bottom left Same as upper right, but normalization were done based on the integral within [-4,4] bins around maximum.

4. Bottom right Same as for upper right, but with a different normalization ([-4,4] bins around maximum)

I have also tried to normalized by the total integral, but the results looks similar.

 

Figure 1: See description above

 

Figure 2: Same without log scale (See description above)

2008.04.09 Applying gamma-jet reconstruction algorithm for gamma-jet simulated events

Ilya Selyuzhenkov April 09, 2008

Data sample:
Monte-Carlo gamma-jet sample for partonic pt range of 5-7, 7-9, 9-11,11-15, 15-25, 25-35 GeV.

Analysis: Simulated MuDst files were first processed through jet finder algorithm (thanks to Renee Fatemi),
and later analyzed by applying gamma-jet isolation cuts (see this link for details) and studying EEMC SMD response (see below).
To test the algorithm, Geant records were not used in this analysis.
Further studies based on Geant records (yield estimates, etc) are ongoing.

EESMD shapes comparison

Figure 1:Comparison between shower shape profile for data and MC.
Black circles shows results for MC gamma-jet sample (all partonic pt).
For v-plane results see this figure

 

Correlation between gamma and jet pt, eta, phi

Figure 2:Gamma vs jet transverse momentum.

 

Figure 3:Gamma vs jet azimuthal angle.

 

Figure 4:Gamma vs jet pseudo-rapidity.

 

Results from maximum sided residua study

Definitions for F_peak, D_peak, D_tail^max (D_tail^min) can be found here

Figure 5:F_peak vs maximum residual
for various cuts on energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).
Shower shape used to fit data is fixed to the shape from the previous gamma-jet study of real events
(see black point on Fig.1 [upper left plot] at this page)

 

Figure 6: F_peak vs D_tail^max: click here
Figure 7: F_peak vs D_tail^max-D_tail^min: click here

Postshower to SMD[uv] energy ratio

Figure 8:Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

 

Figure 8a:
Same as figure 8, but for gamma-jet candidates from the real data (no pt cuts).
Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

 

Figure 8b:
Comparison between gamma-jet candidates from data with different preshower conditions.
Points are normalized in peak to the case of pre1 > 0, pre2 > 0
Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

 

Figure 8c:
Comparison between gamma-jet candidates from Monte-Carlo simulations with different preshower conditions.
Points are normalized in peak to the case of pre1 > 0, pre2 > 0
Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

 

Additional QA plots

Figure 9: Jet neutral energy fraction
Figure 10: High v-strip vs u-strip
Figure 11: energy post shower (3x3 cluster)
Figure 12: Peak energy SMD-u
Figure 13: Peak energy SMD-v
Figure 14: Gamma phi
Figure 15: Gamma pt
Figure 16: Gamma eta
Figure 17: Delta gamma-jet pt
Figure 18: Delta gamma-jet eta
Figure 19: Delta gamma-jet phi

 

chi2 distributions

Figure 20:chi2 distribution using "standard" MC shape

 

Figure 21:chi2 distribution using Pibero shape

2008.04.16 Sided residual: Data Driven MC vs raw MC vs 2006 data

Ilya Selyuzhenkov April 16, 2008

Figure 1: Sided residual for raw MC (partonic pt 9-11)

 

Figure 2: Sided residual for data-driven MC (partonic pt 9-11)

 

Figure 3: Sided residual for data (pp Longitudinal 2006)

 

Different analysis cuts vs number of events which passed the cut

1. N_events : total number of di-jet events found by the jet-finder for gamma in eta region [1,2]
   (Geant record is used to get this number)
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy.
4. R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet
5. N_ch=0 : no charge tracks associated with a gamma candidate
6. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
7. N_(5-strip clusler)^u > 3 : minimum number of strips in EEMC SMD u-plane cluster around peak
8. N_(5-strip cluster)^v > 3 : minimum number of strips in EEMC SMD v-plane cluster around peak
9. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
10. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluser

Figure 4: Number of events which passed various cuts (MC data, partonic pt 9-11)

 

2008.04.17 Sided residual: Data Driven MC vs raw MC (partonic pt=5-35) vs 2006 data

Ilya Selyuzhenkov April 17, 2008

MC data for different pt weigted according to Michael Betancourt web page:
weight = xSection[ptBin] / xSection[max] / nFiles

Figure 1: Sided residual for raw MC (partonic pt 5-35)
(same plot for partonic pt 9-11)

 

Figure 2: Sided residual for data-driven MC (partonic pt 5-35)
(same plot for partonic pt 9-11)

 

Figure 3: Sided residual for data (pp Longitudinal 2006)

 

Figure 4: Sided residual for data (pp Longitudinal 2006)

 

Figure 5: Sided residual for data (pp Longitudinal 2006)

 

Figure 6: pt(gamma) from geant record vs
pt(gamma) from energy in 3x3 tower cluster and position for uv-intersection wrt vertex
(same on a linear scale)

 

Figure 7: pt(gamma) from geant record vs
pt(jet) as found by the jet-finder

 

Figure 8: gamma pt distribution:
data-driven MC (red) vs gamma-jet candidates from pp2006 longitudinal run (black).
MC distribution normalized to data at maximum for each preshower condition

 

Different analysis cuts vs number of events which passed the cut

1. N_events : total number of di-jet events found by the jet-finder for gamma in eta region [1,2]
   (Geant record is used to get this number)
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy.
4. R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet
5. N_ch=0 : no charge tracks associated with a gamma candidate
6. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
7. N_(5-strip clusler)^u > 3 : minimum number of strips in EEMC SMD u-plane cluster around peak
8. N_(5-strip cluster)^v > 3 : minimum number of strips in EEMC SMD v-plane cluster around peak
9. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
10. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluser

Figure 9: Number of events which passed various cuts (MC data, partonic pt 5-35)
Red: cuts applied independent
Black: cuts applied sequential from left to right

 

2008.04.23 Gamma-jet candidates: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet)

Ilya Selyuzhenkov April 23, 2008

Sided residual: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet)

MC data for different partonic pt are weigted according to Michael Betancourt web page:
weight = xSection[ptBin] / xSection[max] / nFiles

Figure 1:Sided residual for data-driven gamma-jet MC events (partonic pt 5-35)

 

Figure 2:Sided residual for data-driven jet-jet MC events (partonic pt 3-55)

 

Figure 3:Sided residual for data (pp Longitudinal 2006)

 

Figure 4:pt(gamma) vs pt(jet) for data-driven gamma-jet MC events (partonic pt 5-35)

 

Figure 5:pt(gamma) vs pt(jet) for data-driven jet-jet MC events (partonic pt 3-55)

 

Figure 6:pt(gamma) vs pt(jet) for data (pp Longitudinal 2006)

2008.05.05 pt-distributions, sided residual (data vs dd-MC g-jet and bg di-jet)

Ilya Selyuzhenkov May 05, 2008

Data samples:

• pp2006(long) - 2006 pp production longitudinal data after applying gamma-jet aisolation cuts
  (jet-tree sample: 4.114pb^-1 from Jamie script, 3.164 pb^1 analyses).
• gamma-jet - Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV
• bg jets - Pythia di-jet sample (~4M events). Partonic pt range 3-65 GeV

Figure 1:pt distribution. MC data are scaled to the same luminosity as data
(Normalization factor: Luminosity * sigma / N_events).

 

 

Figure 2:Integrated gamma yield vs pt.
For each pt bin yield is defined as the integral from this pt up to the maximum available pt.
MC data are scaled to the same luminosity as data.

 

Figure 3:Signal to background ratio (all results divided by the data)

 

Sided residual: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet)

You can find sided residual 2-D plots here

Figure 4:Maximum sided residual for pt_gamma>7GeV; pt_jet>7GeV

 

Figure 5:Fitted peak for pt_gamma>7GeV; pt_jet>7GeV

 

Figure 6:Max data tail for pt_gamma>7GeV; pt_jet>7GeV

 

Figure 7:Max minus min data tails for pt_gamma>7GeV; pt_jet>7GeV

 

Figure 8:Shower shapes pt_gamma>7GeV; pt_jet>7GeV

2008.05.08 y:x EEMC position for gamma-jet candidates

Ilya Selyuzhenkov May 08, 2008

y:x EEMC position for gamma-jet candidates

Figure 1:y:x EEMC position for gamma-jet candidates:
Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.

 

Figure 2:y:x EEMC position for gamma-jet candidates:
Pythia QCD bg sample (~4M events). Partonic pt range 3-65 GeV.

 

Figure 3:y:x EEMC position for gamma-jet candidates:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]

 

Figure 3b:y:x EEMC position for gamma-jet candidates:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

high u vs. v strip for gamma-jet candidates

 

Figure 4:High v-strip vs high u-strip.
Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.

Figure 5:High v-strip vs high u-strip:
Pythia QCD bg sample (~4M events). Partonic pt range 3-65 GeV.

 

Figure 6:High v-strip vs high u-strip:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]

 

Figure 6b:High v-strip vs high u-strip:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

 

2008.05.09 Gamma-jet candidates pt-distributions and TPC tracking

Ilya Selyuzhenkov May 09, 2008

Detector eta cut study (1< eta < 1.4):

• For a three data samples (pp2006 [long], MC gamma-jet, and MC QCD background events)
  the EEMC detector eta cut of 1< eta < 1.4 has been applied.

• Although a poor statistics available for MC background QCD sample,
  the signal to background ratio (red to green line ratio)
  getting closer to 1:3 (expected signal to background ratio from Les study).

• Distributions withoud EEMC detector eta cut.

• y:x position within EEMC detector for gamma-jet candidates.

Figure 1:Gamma pt distribution. MC data are scaled to the same luminosity as data
(Normalization factor: Luminosity * sigma / N_events).

 

Figure 2:Gamma yield vs pt. MC data are scaled to the same luminosity as data.

 

Figure 3:Signal to background ratio (MC results are normalized to the data)

2008.05.14 Gamma-cluster to jet energy ratio and away side jet pt matching

Ilya Selyuzhenkov May 14, 2008

Gamma-cluster to jet1 energy ratio

• Correlation between gamma-candidate 3x3 cluster energy ratio (R_cluster) and
  number of EEMC towers in a jet1 can be found here (Fig. 4).

• Gamma pt distribution, yield and signal to background ratio plots
  for a cut of R_cluster >0.9 can be found here (Figs. 1-3).

• Gamma pt distribution, yield and signal to background ratio plots
  for a cut of R_cluster >0.99 are shown below in Figs. 1-3.
  One can see that by going from R_cluster>0.9 to R_cluster>0.99
  improves signal to background ratio from ~ 1:10 to ~ 1:5 for gamma pt>10 GeV

Figure 1:Gamma pt distribution for R_cluster >0.99.
MC results scaled to the same luminosity as data
(Normalization factor: Luminosity * sigma / N_events).

 

Figure 2:Integrated gamma yield vs pt for R_cluster >0.99
For each pt bin yield is defined as the integral from this pt up to the maximum available pt.
MC results scaled to the same luminosity as data.

 

Figure 3:Signal to background ratio for R_cluster >0.99 (all results divided by the data)
Compare this figure with that for R_cluster>0.9 (Fig. 3 at this link)

 

Gamma and the away side jet pt matching

Figure 4: pt asymmetry between gamma and the away side jet (R_cluster >0.9)
for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background).
pt cut of 7 GeV for both gamma and jet has been applied.

Figure 5: signal to background ratio (R_cluster >0.9)
as a function of pt asymmetry between gamma and the away side jet
pt cut of 7 GeV for both gamma and jet has been applied.

 

 

Figure 6: pt asymmetry between gamma and the away side jet (R_cluster >0.99)
for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background).
pt cut of 7 GeV for both gamma and jet has been applied.

Figure 7: signal to background ratio
as a functio of pt asymmetry between gamma and the away side jet (R_cluster >0.99)
pt cut of 7 GeV for both gamma and jet has been applied.

 

 

Figure 8: pt asymmetry between gamma and the away side jet (R_cluster >0.99)
for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background).
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

Figure 9: signal to background ratio
as a function of pt asymmetry between gamma and the away side jet (R_cluster >0.99)
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

 

2008.05.15 Vertex z distribution for pp2006 data, MC gamma-jet and QCD jets events

Ilya Selyuzhenkov May 15, 2008

Figure 1:Vertex z distribution for pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]
Note: In the upper right plot (pre1=0, pre2>0) one can see
a hole in the acceptance in the range bweeeen z_vertex -10 to 30 cm (probably due to SVT construction)

 

Figure 1b:Vertex z distribution for pp2006 (same as Fig. 1, but on a linear scale)

 

Figure 2:Vertex z distribution for three different data samples
MC results scaled to the same luminosity as data

 

Figure 3:Vertex z distribution for three different data samples
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

2008.05.20 Shower shapes sorted by pre-shower, z-vertex and gamma's eta, phi, pt

Ilya Selyuzhenkov May 20, 2008

Gamma-jet algorithm and isolation cuts:

1. Selecting only di-jet events identified by the STAR jet finder algorithm,
   with jets pointing opposite in azimuth:
   cos(phi_jet1 - phi_jet2) < -0.8

2. Select jet1 with a maximum neutral energy fraction (R_EM1).
   This is our gamma candidate, for which we further require:
   • No charge tracks associated with jet1 (default jet radius is 0.7):
     nChargeTracks_jet1 = 0
     Note, that this charge track veto only works
     in the EEMC region where we do have TPC tracking
   • No barrel towers associated with jet1 (pure EEMC jet):
     nBarrelTowers_jet1 = 0
   • Ratio of the energy in the 3x3 EEMC high tower cluster
     to the total jet energy to be:
     R_cluster>0.99 (previous, softer, cut was 0.9)

    

3. For the second jet2 (away side jet) we require:
   • That jet2 has at least ~10% of hadronic energy:
     R_EM2<0.9

    

4. Additional gamma candidate QA requirements:
   • Matching between EEMC SMD uv-strip cluster with a 3x3 cluster of EEMC towers.
     (in addition reject events for which we can not idetify uv-strip intersection)
   • Minimum number of strips in 5-strip EEMC SMD uv-plane clusters to be greater that 3.

Data sample:

• pp2006(long) - 2006 pp production longitudinal data after applying gamma-jet isolation cuts
  (note the new R_cluster>0.99 cut)

Shower shapes sorted by pre-shower, z-vertex and gamma's eta, phi, pt

Note, that all shapes are normalized at peak to unity

Figure 1:Shower shapes for different detector eta bins

 

Figure 2:Shower shapes for different detector phi bins

 

Figure 3:Shower shapes for different gamma pt bins

 

Figure 4:Shower shapes for different z-vertex bins

 

2008.05.21 EEMC SMD data-driven library: some eta-meson QA plots

Ilya Selyuzhenkov May 21, 2008

EEMC SMD data-driven library: some eta-meson QA plots

Data sample:

• Subset of 441 eta-meson candidates from Will's analysis.

• additional QA info (detector eta, pre1, pre2, etc)
  has been added to pi0-tree reader script:
  /star/institutions/iucf/wwjacobs/newEtas_fromPi0finder/ReadEtaTree.C

• pi0 trees from this RCF directory has been used to regenerate etas NTuple:
  /star/institutions/iucf/wwjacobs/newEtas_fromPi0finder/out_23/

Some observations:

• eta-meson purity within the invariant mass region [0.5, 0.65] is about 72%

• Most of the eta-candidates has detector pseudorapidity less or about 1.4,
  what may limits applicability of data-driven shower shapes
  derived from these candidates for higher pseudo-rapidity region,
  where we have most of the background for the gamma-jet
  analysis due to lack of TPC tracking

• z-vertex distribution is very asymmetric, and peaked around -50cm.
  Only a few candidates has a positive z-vertex values.

Figure 1: Eta-meson invariant mass with signal and background fits and ratio (upper left).
Pseudorapidity [detector and wrt vertex] distributions (right top and bottom plots),
vertex z distributions (bottom left)

 

Figure 2:2D plots for the eta-meson invariant mass vs
azimuthal angle (upper left), pseudorapidity (upper right),
z-vertex (bottom right), and detector pseudorapidity (bottom right)

 

2008.05.27 Shower shapes: pp2006 data, MC gamma-jet and QCD jets, gammas from eta

Ilya Selyuzhenkov May 27, 2008

Shower shapes and triple Gaussian fits for gammas from eta-meson

Figure 1: Shower shapes and triple Gaussian fits for photons from eta-meson
sorted by different conditions of EEMC 1st and 2nd pre-shower layers.
Note: All shapes have been normalized at peak to unity

 

Triple Gaussian fit parameters:
Pre1=0 Pre2=0
0.669864*exp(-0.5*sq((x-0.46016)/0.574864))+0.272997*exp(-0.5*sq((x-0.46016)/-1.84608))+0.0585682*exp(-0.5*sq((x-0.46016)/5.49802))
Pre1=0 Pre2>0
0.0694729*exp(-0.5*sq((x-0.493468)/5.65413))+0.615724*exp(-0.5*sq((x-0.493468)/0.590723))+0.314777*exp(-0.5*sq((x-0.493468)/2.00192))
Pre1>0 Pre2>0
0.0955638*exp(-0.5*sq((x-0.481197)/5.59675))+0.558661*exp(-0.5*sq((x-0.481197)/0.567596))+0.345896*exp(-0.5*sq((x-0.481197)/1.9914))

 

Shower shapes: pp2006, MC gamma-jet and QCD jets, gammas from eta

Shower shapes comparison between different data sets:

• gammas from eta-meson decay. Obtained from Will's eta-meson analysis
• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Some observations:

• Shapes for gammas from eta-meson decay
  are in a good agreement with those from MC gamma-jet sample
  (compare red squares with blue triangle in Fig. 2 and 3).

  MC gamma-jet shapes obtained by running a full gamma-jet reconstruction algorithm,
  and this agreement indicates that we are able to reconstruct gamma shapes
  which we put in with data-driven shower shape library.

• MC gamma-jet shapes match pp2006 data shapes
  for pre1=0 condition, where we expect to be very efficient in background rejection
  (compare red squares with black circles in upper plots of Fig. 2 and 3).

  This indicates that we are able to reproduce EEMC SMD of direct photons with data-driven Monte-Carlo.

• There is no match between Monte-Carlo QCD background jets and pp2006 data
  for the case when both pre-shower layer fired (pre1>0 and pre2>0).
  (compare green triangles with black circes in bottom right plots of Fig.2 and 3).
  This is the region where we know background dominates our gamma-jet candidates.

  This shows that we still do not reproduce SMD response for our background events
  in our data-driven Monte-Carlo simulations
  (note, that in Monte-Carlo we replace SMD response with real shapes for all background photons
  the same way we do it for direct gammas).

Figure 2: Shower shapes comparison between different data sets.
Shapes for gamma-jet candidates obtained with the same gamma-jet reconstruction algorithm
for three different data samples (pp2006, gamma-jet and QCD jets MC).
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 3:Same as Fig. 2, but with no cuts on gamma and jet pt.
All shapes are similar to those in Fig. 2 with an additional pt cuts.
Note, that blue triangles are the same as in Fig. 2.

 

2008.05.30 Eta, phi, and pt distributions for gamma and jet from MC and pp2006 data

Ilya Selyuzhenkov May 30, 2008

Three data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Figure 1: Gamma eta distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 2: Gamma pt distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 3: Gamma phi distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 4: Away side jet eta distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 5: Away side jet pt distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 6: Gamma-jet delta pt distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 7: Gamma-jet delta eta distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 8: Gamma-jet delta phi distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

2008.06.04 Gamma cluster energy in various EEMC layers: data vs MC

Ilya Selyuzhenkov June 04, 2008

Gamma cluster energy in various EEMC layers: data vs MC

Three data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Figure 1: Gamma candidate EEMC pre-shower 1 energy (3x3 cluster).
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 2: Gamma candidate EEMC pre-shower 2 energy (3x3 cluster).
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 3: Gamma candidate EEMC tower energy (3x3 cluster).
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 4: Gamma candidate EEMC post-shower energy (3x3 cluster).
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 5: Gamma candidate EEMC SMD u-plane energy [5-strip cluster] (Figure for v-plane)
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Difference between total and gamma candidate cluster energy for various EEMC layers

Figure 6: Total minus gamma candidate (3x3 cluster) energy in EEMC pre-shower 1 layer
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 7: Total minus gamma candidate (3x3 cluster) energy in EEMC pre-shower 2 layer
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 8: Total minus gamma candidate (3x3 cluster) energy in EEMC tower
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 9: Total minus gamma candidate (3x3 cluster) energy in EEMC post-shower layer
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

 

Figure 10: Total (sector) energy minus gamma candidate (5-strip cluster) energy in EEMC SMD[u-v] layer
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

2008.06.09 STAR White paper plots (pt distribution: R_cluster 0.99 and 0.9 cuts)

Ilya Selyuzhenkov June 09, 2008

Gamma pt distribution: data vs MC (R_cluster 0.99 and 0.9 cuts)

Three data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Numerical values for different pt-bins from Fig. 1-2

Figure 1: Gamma pt distribution for R_cluster >0.9.
No energy in both pre-shower layer (left plot), and
No energy in pre-shower1 and non-zero energy in pre-shower2 (right plot)
Same figure for R_cluster>0.99 can be found here

 

Figure 2: Gamma pt distribution for R_cluster >0.9.
No energy in first EEMC pre-shower1 layer (left plot), and
non-zero energy in pre-shower1 (right plot)
For more details (yield, ratios, all pre12 four conditions, etc) see figures 1-3 here.

 

Figure 3: Gamma pt distribution for R_cluster >0.99.
For more details (yield, ratios, all pre12 four conditions, etc) see figures 1-3 here.

2008.06.10 Gamma-jet candidate longitudinal double spin asymmetry

Ilya Selyuzhenkov June 10, 2008

Note: No background subtraction has been done yet

The case of pre-shower1=0 (left plots) roughly has 1:1 signal to background ratio,
while pre-shower1>0 (right plots) have 1:10 ratio (See MC to data comparison for details).

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts,
  plus two additional vertex QA cuts:
  a) |z_vertex| < 100 and
  b) 180 < bbcTimeBin < 300
• Polarization fill by fill: blue and yellow
• Relative luminosity by polarization fills and runs: relLumi06_070614.txt.gz
• Equations used to calculate A_LL from the data: pdf file

Figure 1: Gamma-jet candidate A_LL vs gamma pt.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7
(see pdf/html links above for epsilon_LL and 1/Lum definitions)

 

Figure 2: Gamma-jet candidate A_LL vs x_gluon.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7

 

Figure 3: Gamma-jet candidate A_LL vs x_quark.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7

 

Figure 4: Gamma-jet candidate A_LL vs away side jet pt.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7

2008.06.18 Photon-jet reconstruction with the EEMC detector (talk at the STAR Collaboration meeting)

Ilya Selyuzhenkov June 18, 2008

Slides

Photon-jet reconstruction with the EEMC detector - Part 1: pdf or odp

Talk outline (preliminary)

1. Introduction and motivation

2. Data samples (pp2006, MC gJet, MC QCD bg)
   and gamma-jet reconstruction algorithm:

   • di-jets, isolation cuts, EEMC 3x3 cluster
   • Data sets used and sample EEMC response to a photon candidate

3. Comparing pp2006 with Monte-Carlo simulations scaled to the same luminosity
   (EEMC pre-shower sorting):

   • Gamma pt distribution
   • gamma and jet pt, eta, phi, gamma-jet delta_pt, eta, phi distributions
   • Energy distributions in various EEMC layers

4. EEMC SMD shower shapes from different data samples
   (pp2006 and data-driven Monte-Carlo):

   • Shower shapes sorted by energy in pre-shower 1 and 2 layers
   • pp2006 shower shapes sorted by vertex z, detector eta, phi, pt

5. Sided residual plots: pp2006 vs data-driven Monte-Carlo
   (gammas from eta meson: 3 gaussian fits)

   • Sided residual: 2D correlations
   • Sided residual: 1D projections

6. Various cuts study:

   • R_cluster cut of 0.99 and 0.9 comparison
     (Cluster energy distribution vs number of EEMC towers fired: Fig.4)
   • Charge particle veto and TPC tracking
   • Away side jet pt matching

7. Some QA plots:

   • Gamma candidate u- vs. v- plane high strip distribution; x:y position
   • Vertex z distribution

8. A_LL reconstruction technique:

   • Method and gamma-jet candidates raw asymmetry

9. Work in progress... To do list:

   • Understading MC background and pp2006 data shower shapes discrepancy
   • Implementing sided residual technique with shapes sorted by pre1&2 (eta, sector, etc?)
   • Tuning analysis cuts
   • Quantifying signal to background ratio
   • Background subtraction for A_LL, ...
   • What else?
10. Talk summary

 

2008.07.07 Pre-shower1 < 5MeV cut study

Ilya Selyuzhenkov July 07, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Figure 1: Correlation between 3x3 cluster energy in pre-shower2 vs. pre-shower1 layers

 

Figure 1a: Distribution of the 3x3 cluster energy in pre-shower1 layer (zoom in for Epre1<0.03 region)
(pp2006 data vs. MC gamma-jet and QCD events)

 

Figure 2: Shower shapes after pre-shower1 < 5MeV cut.
Shapes are narrower than those without pre1 cut (see Fig. 2)

 

 

Figure 3: Gamma pt distribution with pre-shower1 < 5MeV cut.
Compare with distribution withoud pre-shower1 (Fig. 3)

 

Sided residual (before and after pre-shower1 < 5MeV cut)

Figure 4: Fitted peak vs. maximum sided residual (no pre-shower1 cuts)
Only points for pp2006 data are shown.

 

Figure 5: Fitted peak vs. maximum sided residual (after pre-shower1 < 5MeV cut).
Only points for pp2006 data are shown.
Note that distribution for pre1>0,pre2>0 case are narrower
compared to that in Fig.4 (without pre-shower1 cuts).

 

Figure 6: Distribution of maximum sided residual with pre-shower1 < 5MeV cut.

2008.07.16 Gamma-gamma invariant mass cut study

Ilya Selyuzhenkov July 16, 2008

Three data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

My simple gamma-gamma finder is trying to
find a second peaks (clusters) in each SMD u and v planes,
match u and v plane high strip intersections,
and calculate the invaraint mass from associated tower energies (3x3 cluster)
according to the energy sharing between SMD clusters.

Figure 1: Gamma-gamma invariant mass plot.
Only pp2006 data are shown: black: no pt cuts, red: gamma pt>7GeV and jet pt>5 GeV.
Clear pi0 peak in the [0.1,0.2] invariant mass region.
Same data on the log scale

 

Gamma pt distributions

Figure 2: Gamma pt distribution (no inv mass cuts).

 

Figure 3: Gamma pt distribution (m_invMass<0.11 or no second peak found).
This cut improves signal to background ratio.

 

Figure 4: Gamma pt distribution (m_invMass>0.11).
Mostly background events.

 

Shower shapes

Figure 5: Shower shapes (no pre1 and no invMass cuts).
Good match between shapes in case of no energy in pre-shower1 layer (pre1=0 case).

 

Figure 6: Shower shapes (pre1<5MeV, no invMass cuts).
For pre1&2>0 case shapes getting closer to ech other, but still do not match.

 

Figure 7: Shower shapes (cuts: pre1<5MeV, invMass<0.11 or no second peak found).
Note, the surprising agreement between eta-meson shapes (blue) and data (black).

 

Gamma-gamma invariant mass plots

Figure 8: Invariant mass distribution (MC vs. pp2006 data): no pre1 cut

 

Figure 9: Invariant mass distribution (MC vs. pp2006 data): pre1<5MeV
Left side is the same as in Figure 8

 

Figure 10: Invariant mass distribution (MC vs. pp2006 data): pre1>5MeV
Left side plot is empty, since there is no events with [pre1=0 and pre1>5MeV]

2008.07.22 Photons from eta-meson: library QA

Ilya Selyuzhenkov July 22, 2008

Shower shapes

Figure 1: Shower shapes: no energy cuts, only 12 strips from peak (left u-plane, right v-plane).

Figure 1a: Shower shapes: no energy cuts, 150 strips from peak (left u-plane, right v-plane).

 

Figure 2: Shower shapes Energy>8GeV (left u-plane, right v-plane).

 

Figure 3: Shower shapes Energy<=8GeV (left u-plane, right v-plane).

 

One dimensional distributions

Figure 4: Tower energy.

 

Figure 5: Post-shower energy.

 

Figure 6: Pre-shower1 energy.

 

Figure 7: Pre-shower2 energy.

 

Figure 8: Number of library candidates per sector.

 

Correlation plots

Figure 9: Transverse momentum vs. energy.

 

Figure 10: Distance from center of the detector vs. energy.

 

Figure 11: x:y position.

 

Figure 12: u- vs. v-plane position.

2008.07.29 Shower shape comparison with new dd-library bins

Ilya Selyuzhenkov July 29, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

 

Latest data-driven shower shape replacement library:

• Four pre-shower bins: pre1,2=0, pre1=0,pre2>0 pre1<4MeV, pre1>=4MeV
• plus two energy bins (E<8GeV, E>=8GeV)

 

Figure 1: Shower shapes for u-plane [12 strips]
Shower shapes for the library are for the E>8GeV bin.

 

Figure 2: Shower shapes for v-plane [12 strips]

 

Figure 3: Shower shapes for u-plane [expanded to 40 strips]

 

Figure 4: Shower shapes for v-plane [expanded to 40 strips]

 

2008.08.14 Shower shape with bug fixed dd-library

Ilya Selyuzhenkov August 14, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

 

Data-driven maker with bug fixed multi-shape replacement:

• Four pre-shower bins: pre1,2=0, pre1=0,pre2>0 pre1<4MeV, pre1>=4MeV
• plus two energy bins (E<8GeV, E>=8GeV)

 

Figure 1: Shower shapes for u-plane [12 strips]
Shower shapes for the library are for the E>8GeV bin.
Open squares and triangles represents raw [without dd-maker]
MC gamma-jet and QCD background shower shapes respectively

 

Figure 2: Shower shapes for v-plane [12 strips]

 

Figure 3: Shower shapes for u-plane [expanded to 40 strips]
Dashed red and green lines represents raw [without dd-maker]
MC gamma-jet and QCD background shower shapes respectively

 

Figure 4: Shower shapes for v-plane [expanded to 40 strips]

 

2008.08.19 Shower shape from pp2008 vs pp2006 data

Ilya Selyuzhenkov August 19, 2008

Data sets:

• pp2006 - STAR 2006 ppProductionLong data (~ 3.164 pb^1)
  "eemc-http-mb-l2gamma" trigger after applying gamma-jet isolation cuts.
• pp2008 - STAR ppProduction2008 (~ 5.9M events)
  "fmsslow" trigger after applying gamma-jet isolation cuts.
  [Only ~13 candidates has been selected before pt-cuts]
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Note: Due to lack of statistics for 2008 data, no pt cuts applied on gamma-jet candidates for both 2006 and 2008 date.

Figure 1: Shower shapes for u-plane [pp2006 data: eemc-http-mb-l2gamma trigger]

 

Figure 2: Shower shapes for v-plane [pp2006 data: eemc-http-mb-l2gamma trigger]

 

Figure 3: Shower shapes for u-plane [pp2008 data: fmsslow trigger]

 

Figure 4: Shower shapes for v-plane [pp2008 data: fmsslow trigger]

 

2008.08.25 di-jets from pp2008 vs pp2006 data

Ilya Selyuzhenkov August 25, 2008

Data sets:

• pp2006 - ppProductionLong [triggerId:137213] (day 136 only)
• pp2008 - ppProduction2008 [triggerId:220520] (Jan's set of MuDst from day 047)

Event selection:

• Run jet finder and select only di-jet events [adopt jet-finder script from Murad's analysis]
• Define jet1 as the jet with largest neutral energy fraction (NEF), and jet2 - the jet with a smaller NEF
• Require no EEMC towers associated with jet1
• Select trigger (see above) and require vertex to be found

Figure 1: Vertex z distribution (left: pp2008; right: 2006 data)

Figure 2: eta vs. phi distribution for the jet1 (jet with largest NEF) .

Figure 3: eta vs. z-vertex distribution for the jet1 (jet with largest NEF) .

Figure 4: eta vs. z-vertex distribution for the second jet.

Figure 5: Transverse momentum distribution for jet1.

Figure 6: Number of barrel towers associated with jet1.

Figure 7: Number of charge tracks associated with jet1.

2008.08.26 Shower shape: more constrains for pre1>4E-3 bin

Ilya Selyuzhenkov August 26, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

 

Data-driven library:

• Four pre-shower bins: pre1,2=0, pre1=0,pre2>0 pre1<4MeV, pre1>=4MeV
• plus two energy bins (E<8GeV, E>=8GeV)

 

Figure 1: Pre-shower1 energy distribution for Pre1>4 MeV:
Eta meson library for E>8GeV bin [left] and data vs. MC results [right].

 

Figure 2: Shower shapes for v-plane [Pre1<10MeV cut]

Figure 3: Shower shapes for u-plane [Pre1<10MeV cut]

Maximum side residual plots

Definitions for side residual plot (F_peak, F_tal, D_tail) can be found here
For a moment same 3-gaussian shape is used to fit SMD response for all pre-shower bins.
Algo needs to be updated with a new shapes sorted by pre-shower bins.

Figure 4: Sided residual plot for pp2006 data only [Pre1<10MeV cut]

Figure 5: Sided residual projection on "Fitted Peak" axis [Pre1<10MeV cut]

Figure 6: Sided residual projection on "tail difference" axis [Pre1<10MeV cut]

2008.08.27 Gamma-jet candidates detector position for different pre-shower conditions

Ilya Selyuzhenkov August 27, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Figure 1: High u vs. v strip id distribution for different pre-shower conditions.
Left column: QCD jets, middle column: gamma-jet, right columnt: pp2006 data

Figure 2: x vs. y position of the gamma-candidate within EEMC detector
for different pre-shower conditions.
Left column: QCD jets, middle column: gamma-jet, right columnt: pp2006 data

Figure 3:Reconstructed vs. generated (from geant record) gamma pt for the MC gamma-jet sample.
Pre-shower1<10MeV cut applied.

Figure 4:Reconstructed vs. generated (from geant record) gamma eta for the MC gamma-jet sample.
Pre-shower1<10MeV cut applied.

Figure 5:Reconstructed vs. generated (from geant record) gamma phi for the MC gamma-jet sample.
Pre-shower1<10MeV cut applied.

2008.09.02 Shower shape fits

Ilya Selyuzhenkov September 02, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Shower shape fitting procedure:

1. Fit with single Gaussian shape using 3 highest strips
2. Fit with double Gaussian using 5 strips from each side of the peak [11 strips total]
   First Gaussian parameters are fixed from the step above
3. Re-fit with double Gaussian with initial parameters from step 2 above
4. Fit with triple Gaussian [fit range varies from 9 to 15 strips, default is 12 strips, see below]
   Initial parameters for the first two Gaussian are fixed from step 3 above
5. Fit with triple Gaussian with initial parameters from step 4 above
   (releasing all parameters except mean values)

Fitting function "[0]*(exp ( -0.5*((x-[1])/[2])**2 )+[3]*exp ( -0.5*((x-[4])/[5])**2 )+[6]*exp ( -0.5*((x-[7])/[8])**2 ))"

Fit results for MC gamma-jet data sample

Figure 1: MC gamma-jet shower shapes and fits for u-plane
Results from single, double and triple Gaussian fits (using from 9 to 15 strips) are shown.

Figure 2: Same as figure 1. but from v-plane

Figure 3: MC gamma-jet results using triple Gaussian fits within 12 strips from a peak.
Left: u-plane. Right: v-plane

Figure 4: Combined fit results from MC gamma-jet sample

Figure 5: Fitting parameters [see equation for the fit function above].
Note, that parameters 1, 4, and 7 (peak position) has the same value.

Numerical fit results:

1. pre1=0 pre2=0 [u]: 0.602039*((exp(-0.5*sq((x-0.491324)/0.605927))+(0.578161*exp(-0.5*sq((x-0.491324)/2.05454))))+(0.0937517*exp(-0.5*sq((x-0.491324)/6.37656))))
2. pre1=0 pre2=0 [v]: 0.729744*((exp(-0.5*sq((x-0.480945)/0.621631))+(0.327792*exp(-0.5*sq((x-0.480945)/2.01717))))+(0.0410935*exp(-0.5*sq((x-0.480945)/6.49599))))
3. pre1=0 pre2>0 [u]: 0.725212*((exp(-0.5*sq((x-0.474451)/0.560416))+(0.3332*exp(-0.5*sq((x-0.474451)/1.91957))))+(0.0611053*exp(-0.5*sq((x-0.474451)/5.34357))))
4. pre1=0 pre2>0 [v]: 0.686446*((exp(-0.5*sq((x-0.536662)/0.650485))+(0.388429*exp(-0.5*sq((x-0.536662)/1.99118))))+(0.0712328*exp(-0.5*sq((x-0.536662)/5.64637))))
5. 0 <4MeV [u]: 0.612486*((exp(-0.5*sq((x-0.485717)/0.592415))+(0.55846*exp(-0.5*sq((x-0.485717)/1.87214))))+(0.0749598*exp(-0.5*sq((x-0.485717)/6.12462))))
6. 0 <4MeV [v]: 0.651584*((exp(-0.5*sq((x-0.486876)/0.652023))+(0.450767*exp(-0.5*sq((x-0.486876)/2.07667))))+(0.0864232*exp(-0.5*sq((x-0.486876)/5.84357))))
7. 4 <10MeV [u]: 0.621905*((exp(-0.5*sq((x-0.496841)/0.632917))+(0.512575*exp(-0.5*sq((x-0.496841)/1.97482))))+(0.0927374*exp(-0.5*sq((x-0.496841)/6.10844))))
8. 4 <10MeV [v]: 0.634943*((exp(-0.5*sq((x-0.505378)/0.660763))+(0.480929*exp(-0.5*sq((x-0.505378)/2.17312))))+(0.0788037*exp(-0.5*sq((x-0.505378)/6.21667))))

Fit results for pp2006 gamma-jet candidates

Figure 6: Same as Fig. 3, but for gamma-jet candidates from pp2006 data

Figure 7: Same as Fig. 5, but for gamma-jet candidates from pp2006 data

2008.09.09 Maximum sided residual with shower shapes sorted by uv- and pre-shower bins

Ilya Selyuzhenkov September 09, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Procedure to calculate maximum sided residual:

1. For each event fit SMD u and v energy distributions with
   triple Gaussian functions from shower shapes analysis:

   [0]*(exp(-0.5*((x-[1])/[2])**2)+[3]*exp(-0.5*((x-[1])/[4])**2)+[6]*exp(-0.5*((x-[1])/[5])**2))

   Fit parameters sorted by various pre-shower conditions and u and v-planes can be found here
   There are only two free parameters in a final fit: overall amplitude [0] and mean value [1]
   Fit range is +-2 strips from the high strip (5 strips total).

2. Integrate energy from a fit within +-2 strips from high strip.
   This is our peak energy from fit, F_peak.

3. Calculate tail energies on left and right sides from the peak for both data, D_tail, and fit, F_tail.
   Tails are integrated up to 30 strips excluding 5 highest strips.
   Determine maximum difference between D_tail and F_tail:
   max(D_tail-F_tail). This is our maximum sided residual.

4. Plot F_peak vs. max(D_tail-F_tail). This is sided residual plot.

5. (implementation for this item is in progress)
   Based on MC gamma-jet sided residual plot find a line (some polynomial function)
   which will serve as a cut to separate signal and background.
   Use that cut line to calculate signal to background ratio
   and apply it for the real data analysis.

Figure 1: Maximum sided residual plots for different data sets and various pre-shower condition.
Columns [data sets]: 1. MC QCD background; 2. gamma-jet; 3. pp2006 data
Rows [pre-shower bins]: 1. pre1=0 pre2=0; 2. pre1=0, pre2>0; 3. 0<pre1<4MeV; 4. 4<pre1<10MeV
Results from u and v plane are combined as [U+V]/2

Figure 2: max(D_tail-F_tail) distribution (projection on horizontal axis from Fig.1)
Some observations:
Results for pp2006 and MC gamma-jet are consistent for pre1=0 pre2=0 case (upper left plot)
Results for pp2006 and MC QCD background jets are also in agrees for pre1>0 case (lower left and right plots)

Figure 3: F_peak distribution (projection on vertical axis from Fig.1)

2008.09.16 QA plots for maximum sided residual (obsolete)

Ilya Selyuzhenkov September 16, 2008

These results are obsolete.
Please use this link instead

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Notations used in the plots:

• Fit peak energy:
  F_peak - integral within +-2 strips from maximum strip
  Maximum strip determined by fitting procedure.
  Float value converted ("cutted") to integer value.
• Data peak energy:
  D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak).
• Data tails:
  D_tail^left and D_tail^right.
  Energy sum from 3rd strip up to 30 strips on the
  left and right sides from maximum strip (excludes strips which contributes to D_peak)
• Fit tails:
  F_tail^left and F_tail^right.
  Same definition as for D_tail, but integrals are calculated from a fit function.
• Maximum sided residual:
  max(D_tail-F_tail)
  Maximum of the data minus fit energy on the left and right sides from the peak.

Figure 1: D_peak from [U+V]/2.

Figure 2: U/V asymmetry for D_peak: [U-V]/[U+V]

Figure 3: F_peak from [U+V]/2.

Figure 4: U/V asymmetry for F_peak: [U-V]/[U+V]

Figure 5: (D_peak - F_peak)/D_peak asymmetry

Figure 6: Maximum sided residual from V vs. U plane.

Figure 7: (D_tail-F_tail)^right vs. (D_tail-F_tail)^left

2008.09.23 QA plots for maximum sided residual (bug fixed update)

Ilya Selyuzhenkov September 23, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below).
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Notations used in the plots:

• Fit peak energy:
  F_peak - integral within +-2 strips from maximum strip
  Maximum strip determined by fitting procedure.
  Float value converted ("cutted") to integer value.
• Data peak energy:
  D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak).
• Data tails:
  D_tail^left and D_tail^right.
  Energy sum from 3rd strip up to 30 strips on the
  left and right sides from maximum strip (excludes strips which contributes to D_peak)
• Fit tails:
  F_tail^left and F_tail^right.
  Same definition as for D_tail, but integrals are calculated from a fit function.
• Maximum sided residual:
  max(D_tail-F_tail)
  Maximum of the data minus fit energy on the left and right sides from the peak.

Figure 1: D_peak from [U+V]/2.

Figure 2: (D_peak - F_peak)/D_peak asymmetry

Figure 3: Maximum sided residual from V vs. U plane.

Figure 4: (D_tail-F_tail)^right. (D_tail-F_tail)^left

2008.09.23 Right-left SMD tail asymmetries

Ilya Selyuzhenkov September 23, 2008

Figure 1: D_peak vs. [right-left] D_tail

Figure 2: [right-left]/[right-+left] D_tail

2008.09.23 Sided residual plot projection: toward s/b efficency/rejection plot

Ilya Selyuzhenkov September 23, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below).
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Notations used in the plots:

• Fit peak energy:
  F_peak - integral within +-2 strips from maximum strip
  Maximum strip determined by fitting procedure.
  Float value converted ("cutted") to integer value.
• Data peak energy:
  D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak).
• Data tails:
  D_tail^left and D_tail^right.
  Energy sum from 3rd strip up to 30 strips on the
  left and right sides from maximum strip (excludes strips which contributes to D_peak)
• Fit tails:
  F_tail^left and F_tail^right.
  Same definition as for D_tail, but integrals are calculated from a fit function.
• Maximum sided residual:
  max(D_tail-F_tail)
  Maximum of the data minus fit energy on the left and right sides from the peak.

Maximum sided residual: MC vs. data comparison

Figure 1: Maximum sided residual plot
Top get more statistics for MC QCD sample plot is redone with a softer R_cluster > 0.9 cut

Figure 2: D_peak (projection on vertical axis for Fig. 1)
Upper left plot (no pre-shower fired case) reveals some difference
between MC gamma-jet and pp2006 data at lower D_peak values.
This difference could be due to background contribution at low energies.
Still needs more statistics for MC QCD jet sample to confirm that statement.

Figure 3: max(D_tail-F_tail) (projection on horisontal axis for Fig. 1)
One can get an idea of signal/background separation (red vs. black) depending on pre-shower condition.

Figure 4: Mean < max(D_tail-F_tail) > vs. D_peak (profile on vertical axis from Fig. 1)
For gamma-jet sample average sided residual is independent on D_peak energy
and has a slight positive shift for all pre-shower>0 conditions.
For large D_peak values (D_peak>0.16) MC gamma-jet and pp2006 data results are getting close to each other.
This corresponds to higher energy gammas, where we have a better signal/background ratio,
and thus more real gammas among gamma-jet candidates from pp2006 data.
(Note: legend's color coding is wrong, colors scheme is the same as in Fig. 3)

Figure 5: Mean < D_peak > vs. max(D_tail-F_tail) (profile on horisontal axis from Fig. 1)
For "no-preshower fired" case MC gamma-jet sample has a large average values than that from pp2006 data.
This reflects the same difference between pp2006 and MC gamma-jet sample at small D_peak values (see Fig. 2, upper left plot).
(Note: legend's color coding is wrong, colors scheme is the same as in Fig. 3)

Figure 6: D_peak vs. gamma pt

Figure 7: D_peak vs. gamma 3x3 tower cluster energy

Figure 8: 3x3 cluster tower energy distribution

Figure 9: Gamma pt distribution

Signal/background separation

The simplest way to get signal/background separation is to draw a straight line
on sided residual plot (Fig. 1) in such a way that
it will contains most of the counts (signal) on the left side,
and use a distance to that line for both MC and pp2006 data samples
as a discriminant for signal/background separation.
To get the distance to the straight line one can rotate sided residual plot
by the angle which corresponds to the slope of this line,
and then project it on "rotated" max(D_tail-F_tail) axis.

Figure 10: Shows "rotated" sided residual plot by "5/6*(pi/2)" angle (this angle has been picked by eye).
One can see that now most of the counts for gamma-jet sample (middle column)
are on the left side from vertical axis.

Figure 11: "Rotated" max(D_tail-F_tail) [projection on horizontal axis for Fig. 10]
Cut on "Rotated" max(D_tail-F_tail) can be used for signal/background separation.
From figure below one can see much better signal/background separation than in Fig. 3

Figure 12: "Rotated" D_peak [projection on vertical axis for Fig. 10]

Optimizing the shape of s/bg separation line

Ideally, instead of straight line one needs to use
an actual shape of side residual distribution for MC gamma-jet sample.
This shape can be extracted and parametrized by the following procedure:

1. Get slices from sided residual plot for different D_peak values
2. From each slice get max(D_tail-F_tail) value
   for which most of the counts appears on its left side (for example 80%),
3. Fit these set of points {D_peak slice, max(D_tail-F_tail)} with a polynomial function

The distance to that polynomial function can be used to determine our signal/background rejection efficiency.

This work is in progress...
Just last one figure showing shapes for 6 slices from sided plot.

Figure 13: max(D_tail-F_tail) for different slices in D_peak (scaled by the integral for each slice)

2008.09.30 Sided residual: purity, efficiency, and background rejection

Ilya Selyuzhenkov September 30, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below).
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Notations used in the plots:

• Fit peak energy:
  F_peak - integral within +-2 strips from maximum strip
  Maximum strip determined by fitting procedure.
  Float value converted ("cutted") to integer value.
• Data peak energy:
  D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak).
• Data tails:
  D_tail^left and D_tail^right.
  Energy sum from 3rd strip up to 30 strips on the
  left and right sides from maximum strip (excludes strips which contributes to D_peak)
• Fit tails:
  F_tail^left and F_tail^right.
  Same definition as for D_tail, but integrals are calculated from a fit function.
• Maximum sided residual:
  max(D_tail-F_tail)
  Maximum of the data minus fit energy on the left and right sides from the peak.

Determining cut line based on sided residual plot

Figure 1: Sided residual plot: D_peak vs. max(D_tail-F_tail)
Red lines show 4th order polynomial functions, a*x^4,
which have 80% of MC gamma-jet counts on the left side.
These lines are obtained independently for each of pre-shower condition
based on fit procedure shown in Fig. 3 below.

Figure 2: max(D_tail-F_tail) distribution
(projection on horizontal axis from sided residual plot, see Fig. 1 above)

Figure 3: max(D_tail-F_tail) [at 80%] vs. D_peak.
For each slice (bin) in D_peak variable, the max(D_tail-F_tail) value
which has 80% of gamma-jet candidates on the left side are plotted.

Lines represent fits to MC gamma-jet points (shown in red) using different fit functions
(linear, 2nd, 4th order polynomials: see legend for color coding).
Note, that in this plot D_peak values are shown on horizontal axis.
Consequently, to get 2nd order polynomial fit on sided residual plot (Fig. 1),
one needs to use sqrt(D_peak) function.
The same apply to 4th order polynomial function.

Figure 4: D_peak vs. horisontal distance from 4th order polinomial function to max(D_tail-F_tail) values.
(compare with Fig. 1: Now 80% of MC gamma-jet counts are on the left side from vertical axis)

Figure 5: Horizontal distance from 4th order polynomial function to max(D_tail-F_tail)
[Projection on horizontal axis from Fig. 4]
Based on this plot one can obtain purity, efficiency, and rejection plots (see Fig. 6 below)

Gamma-jet purity, efficiency, and QCD background rejection

Horizontal distance plotted in Fig. 5 can be used as a cut
separating gamma-jet signal and QCD-jets background,
and for each value of this distance one can define
gamma-jet purity, efficiency, and QCD-background rejection:

• gamma-jet purity is defined as the ratio of
  the integral on the left for MC gamma-jet data sample, N[g-jet]_left,
  to the sum of the integrals on the left for MC gamma-jet and QCD jets, N[QCD]_left, data samples:
  Purity[gamma-jet] = N[g-jet]_left/(N[g-jet]_left+N[QCD]_left)

• gamma-jet efficiency is defined as the ratio of
  the integral on the left side for MC gamma-jet data sample, N[g-jet]_left,
  to the total integral for MC gamma-jet data sample, N[g-jet]:
  Efficiency[gamma-jet] = N[g-jet]_left/N[g-jet]

• QCD background rejection is defined as the ratio of
  the integral on the right side for MC QCD jets data sample, N[QCD]_right,
  to the total integral for MC QCD jets data sample, N[QCD]:
  Rejection[QCD] = N[QCD]_right/N[QCD]

Figure 6: Shows:
purity[g-jet] vs. efficiency[g-jet] (upper left);
rejection[QCD] vs. efficiency[g-jet] (upper right);
purity[g-jet] vs. rejection[QCD] (lower left);
pp2006 to MC ratio, N[pp2006]/(N[g-jet]+N[QCD]), vs. horizontal distance from Fig. 5 (lower right)

2008.10.13 Jet trees for Michael's gamma filtered events

Ilya Selyuzhenkov October 13, 2008

I have finished production of jet trees for Michael's gamma filtered events

You can find jet and skim file lists in my directory at IUCF disk (RCF):

• Jet trees: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/JetTrees.list
• Skim trees: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/SkimTrees.list
• Log files: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/LogFiles.list

Number of jet events is 1284581 (1020 files).
Production size, including archived log files, is 4.0G.

 

The script to run jet finder:

/star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/20081008_gJet/StRoot/macros/RunJetSimuSkimFinder.C

JetFinder and JetMaker code:

/star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/20081008_gJet/StRoot/StJetFinder
/star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/20081008_gJet/StRoot/StJetMaker

For more details see these threads of discussions:

• jetfinding list: thread 814
• photon reconstruction list: thread 271 and thread 256

 

 

2008.10.14 Purity, efficiency, and background rejection: R_cluster > 0.98

Ilya Selyuzhenkov October 14, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (note: R_cluster > 0.98 is used below).
• gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
• QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Figure 1: Horizontal distance from 4th order polynomial function to max(D_tail-F_tail)
See this page for definition and more details

Figure 2:
purity[g-jet] vs. efficiency[g-jet] (upper left);
rejection[QCD] vs. efficiency[g-jet] (upper right);
purity[g-jet] vs. rejection[QCD] (lower left);
pp2006 to MC ratio, N[pp2006]/(N[g-jet]+N[QCD]), vs. horizontal distance (lower right)

2008.10.15 Comparison of gamma-jets from Michael's filtered events vs. old MC samples

Ilya Selyuzhenkov October 15, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below).
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• gamma-jet [old] - data-driven Pythia gamma-jet sample (~170K events).
  Partonic pt range 5-35 GeV.
  Details on jet trees production for Michael's gamma filtered events can be found here.
• QCD jets [old] - data-driven Pythia QCD jets sample (~4M events).
  Partonic pt range 3-65 GeV.

Some observations:

• Both Fig. 1a vs. Fig. 1b shows good statistics for old and new (gamma-filtered) MC gamma-jet samples
• Fig. 1c shows poor statistics for QCD background sample
  within partonic pt range 5-10GeV (only 3 counts for "pre1=0 & pre2=0" condition).
  Fig. 1d (new QCD sample) has much more counts in the same region,
  but it is still only 20-25 entries for the case when
  none of EEMC pre-shower layers fired (upper left corner - our purest gamma-jet sample).
  This may be still insufficient for a various cuts systematic study.
• Fig. 2 and Fig. 3 shows nice agreement between data and MC
  for both old and new (gamma-filtered) MC samples.
  For pre-shower1>0 case this agreement persists across full range of gamma's pt (7GeV and above).
  Upper plots in Fig. 3 shows some difference between data and Monte-Carlo,
  what could be effect from l2gamma trigger,
  which has not been yet applied for MC events.

Figure 1a: partonic pt for gamma-jet [old] events
after analysis cuts and partonic pt bin weighting
(Note:Arbitrary absolute scale)

Figure 1b: partonic pt for gamma-jet [gamma-filtered] events after analysis cuts.
Michael's StBetaWeightCalculator has been used to caclulate partonic pt weights

Figure 1c: partonic pt for QCD jets [old] events
after analysis cuts and partonic pt bin weighting
(Note:Arbitrary absolute scale)

Figure 1d: partonic pt for QCD jets [gamma-filtered] events after analysis cuts.
Michael's StBetaWeightCalculator has been used to caclulate partonic pt weights

Figure 2: reconstructed gamma pt: old MC vs. pp2006 data (scaled to the same luminosity)

Figure 3: reconstructed gamma pt: gamma-filtered MC vs. pp2006 data (scaled to the same luminosity)

2008.10.15 Purity vs. efficiency from gamma-filtered events: R_cluster > 0.9 vs. R_cluster > 0.98

Ilya Selyuzhenkov October 15, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (note: R_cluster > 0.9 or > 0.98 is used below).
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Gamma-jet candidates from MC gamma filtered events: R_cluster > 0.9

Figure 1: Horizontal distance from sided residual plot: R_cluster > 0.9
(see Figs. 1-5 from this post for horizontal distance definition)

Figure 2: Purity/efficiency/rejection, and data to MC[gamma-jet+QCD] ratio plots: R_cluster > 0.9
(see text above Fig. 6 from this post for purity, efficiency, and background rejection definition)

 

Gamma-jet candidates from MC gamma filtered events: R_cluster > 0.98

Figure 3: Reconstructed gamma pt: R_cluster > 0.98

Figure 4: Horizontal distance from sided residual plot: R_cluster > 0.98

Figure 5: Purity/efficiency/rejection, and data to MC[gamma-jet+QCD] ratio plots: R_cluster > 0.98

2008.10.21 Shower shapes, 5/25 strips cluster energy, raw vs. data-driven MC

Ilya Selyuzhenkov October 21, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (R_cluster > 0.9 is used below).
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Some comments:

• Overall comment: effect of data-driven shower shape replacement procedure
  on QCD background events is small, except probably pre1=0 pre2=0 case.

• Fig. 1-3, upper left plots (pre1=0 pre2=0) show that
  average energy per strip in data-driven gamma-jet MC (i.e. solid red square in Fig. 3)
  is systematically higher than that for pp2006 data (black circles in Fig. 3).

  Note, that there is an agreement between SMD shower shapes
  for pp2006 data and data-driven gamma-jet simulations
  if one scales them to the same peak value
  (Compare red vs. black in upper left plot from Fig. 1 at this link)

• Fig. 4, upper left plot (pre1=0 pre2=0):
  Integrated SMD energy from 25 strips
  in raw gamma-jet simulations (red line) match pp2006 data (black line)
  in the region where signal to background ratio is high, E_smd(25-strips)>0.1GeV.
  This indicates that raw MC does a good job in
  reproducing total energy deposited by direct photon.

• Fig. 5, upper left plot (pre1=0 pre2=0):
  There is mismatch between distributions of energy in 25 strips cluster
  from data-driven gamma-jet simulations and pp2006 data.
  This probably reflects the way we scale our library shower shapes
  in data-driven shower shape replacement procedure.
  Currently, the scaling factor for the library shape is calculated based on the ratio
  of direct photon energy from Geant record to the energy of the library photon.
  Our library is build out of photons from eta-meson decay,
  which has been reconstructed by running pi0 finder.
  The purity of the library is about 70% (see Fig. 1 at this post for more details).

  The improvement of scaling procedure could be to
  preserve total SMD energy deposited within 25 strips from raw MC,
  and use that energy to scale shower shapes from the library.

• Fig. 6, upper left plot (pre1=0 pre2=0):
  Mismatch between integrated 5-strip energy for raw MC and pp2006 in Fig. 6
  corresponds to "known" difference in shower shapes from raw Monte-Carlo and real data.

Figure 1: SMD shower shapes: data, raw, and data-driven MC (40 strips).
Vertical axis shows average energy per strip (no overall shower shapes scaling)

Figure 2: Shower shapes: data, raw, and data-driven MC (12 strips)

Figure 3: Shower shapes: data, raw, and data-driven MC (5 strips)

Figure 4: 25 strips SMD cluster energy for raw Monte-Carlo
(Note: type in x-axis lables, should be "25 strip peak" instead of 5)

Figure 5: 25 strips SMD cluster energy for data-driven Monte-Carlo

Figure 6: 5 strips SMD peak energy for raw Monte-Carlo

Figure 7: 5 strips SMD peak energy for data-driven Monte-Carlo

Figure 8:Energy from the right tail (up to 30 strips) for raw Monte-Carlo

Figure 9:Energy from the right tail (up to 30 strips) for data-driven Monte-Carlo

2008.10.27 SMD-based shower shape scaling: 25 strips cluster energy, raw vs. data-driven MC

Ilya Selyuzhenkov October 27, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (R_cluster > 0.9 is used below).
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Shower shapes scaling options in data-driven maker:

1. scale = E_smd^geant / E_smd^library (default)
   E_smd^geant is SMD energy associated with given photon
   integrated over +/- 12 strips from raw Monte-Carlo,
   and E_smd^library is SMD energy from +/- 12 strips for the library photon.
2. scale = E_Geant / E_library (used before in all posts)
   E_Geant is thrown photon energy from Geant record,
   and E_library is stand for energy of the library photon.

 

In all figures below (exept for pp2006 data and raw Monte-Carlo)
the SMD based shower shape scaling has been used.

Figure 1: SMD shower shapes: data, raw, and data-driven MC (40 strips).
Vertical axis shows average energy per strip (no overall shower shapes scaling)

Figure 2: Shower shapes: data, raw, and data-driven MC (12 strips)

Figure 3: Shower shapes: data, raw, and data-driven MC (5 strips)

Figure 4: 25 strips SMD cluster energy for data-driven Monte-Carlo
(SMD based shower shape scaling)

Figure 5: 25 strips SMD cluster energy for raw Monte-Carlo
Note, the difference between results in Fig. 4 and 5. for MC gamma-jets (shown in red)
at low energy (Esmd < 0.04) for pre1=0 pre2=0 case.
This effect is due to the "Number of strips fired in 5-strips cluster > 3" cut.
In data-driven Monte-Carlo we may have shower shapes
with small number of strips fired (rejected in raw Monte-Carlo)
to be replaced by library shape with different (bigger) number of strips fired.
This mostly affects photons which starts to shower
later in the detector and only fires few strips (pre1=0 pre2=0 case)

2008.10.30 Various cuts study (pt, Esmd, 8 strips replaced)

Ilya Selyuzhenkov October 30, 2008

Below are links to drupal pages
with various SMD energy distributions and shower shapes
for the following set of cuts/conditions:

• Case A: pt > 7 GeV, +/- 12 strips replaced
• Case B: pt > 7 GeV, +/- 8 strips replaced
• Case C: pt > 7 GeV, +/- 12 strips replaced, E_smd(25strips) > 0.1
• Case D: pt > 8.5 GeV, +/- 12 strips replaced

 

2008.11.06 Gamma-jet reconstruction with the Endcap EMC (Analysis status update)

Ilya Selyuzhenkov November 06, 2008

Gamma-jet reconstruction with the Endcap EMC (Analysis status update for Spin PWG)

• latest version (pdf)
• Version 3: pdf or odp (for spin PWG)
• Version 2: pdf or odp (after phana comments)
• Version 1: pdf or odp (draft for phana meeting)

 

2008.11.11 Yields vs. analysis cuts

Ilya Selyuzhenkov November 11, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (R_cluster > 0.9 is used below).
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Figure 1: Reconstructed gamma pt for di-jet events and
Geant cuts: pt_gamma[Geant] > 7GeV and 1.05 < eta_gamma[Geant] < 2.0
Total integral for the histogram is: N_total = 5284
(after weighting different partonic pt bins and scaled to 3.164pb^-1).
Compare with number from Jim Sowinski study for
Endcap East+West gamma-jet and pt>7 GeV: N_Jim = 5472
( Jim's numbers are scaled to 3.164pb^-1 : [2539+5936]*3.164/4.9)

Figure 2: Reconstructed jet pt for di-jet events and the same cuts as in Fig. 1

Yield vs. various analysis cuts

List of cuts (sorted by bin number in Figs. 2 and 3):

1. N_events : total number of di-jet events found by the jet-finder
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy
4. R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet
5. N_ch=0 : no charge tracks associated with a gamma candidate
6. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
7. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
8. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
9. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
10. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster

Figure 3: Number of accepted events vs. various analysis cuts
The starting number of events (shown in first bin of the plots) is
the number of di-jets with reconstructed gamma_pt>7 GeV and jet_pt>5 GeV
upper left: cuts applied independently
upper right: cuts applied sequentially
lower left: ratio of pp2006 data vs. MC sum of gamma-jet and QCD-jets events (cuts applied independently)
lower right:ratio of pp2006 data vs. MC sum of gamma-jet and QCD jets events (cuts applied sequentially)

Figure 4: Number of accepted events vs. various analysis cuts
Data from Fig. 3 (upper plots) scaled to the initial number of events from first bin:
left: cuts applied independently
right: cuts applied sequentially

2008.11.18 Cluster isolation cuts: 2x1 vs. 2x2 vs. 3x3

Ilya Selyuzhenkov November 18, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (no R_cluster cut applied for this study).
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

2x1, 2x2, and 3x3 clusters definition:

• 3x3 cluster: tower energy sum for 3x3 patch around highest tower
• 2x2 cluster: tower energy sum for 2x2 patch
  which are closest to 3x3 tower patch centroid.
  3x3 tower patch centroid is defined based
  on tower energies weighted wrt tower centers:
  centroid = sum{E_tow * r_tow} / sum{E_tow}.
  Here r_tow=(x_tow, y_tow) denotes tower center.
• 2x1 cluster: tower energy sum for high tower plus second highest tower in 3x3 patch
• r=0.7 energy is calculated based on towers
  within a radius of 0.7 (in delta phi and eta) from high tower

Cuts applied

all gamma-jet candidate selection cuts except 3x3/r=0.7 energy isolation cut

 

Results for 2x1, 2x2, and 3x3 clusters

1. Energy fraction in NxN cluster in r=0.7 radius
   2x1, 2x2, 3x3 patch to jet radius of 0.7 energy ratios
2. Yield vs. NxN cluster energy fraction in r=0.7
   For a given cluster energy fraction yield is defined as an integral on the right
3. Efficiency vs. NxN cluster energy fraction in r=0.7
   For a given cluster energy fraction
   efficiency is defined as the yield (on the right)
   normalized by the total integral (total yield)

 

2008.11.21 Energy fraction from 2x1 vs. 2x2 vs. 3x3 or 0.7 radius: rapidity dependence

Ilya Selyuzhenkov November 21, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  after applying gamma-jet isolation cuts (no R_cluster cut applied for this study).
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

2x1, 2x2, and 3x3 clusters definition:

• 3x3 cluster: tower energy sum for 3x3 patch around highest tower
• 2x2 cluster: tower energy sum for 2x2 patch
  which are closest to 3x3 tower patch centroid.
  3x3 tower patch centroid is defined based
  on tower energies weighted wrt tower centers:
  centroid = sum{E_tow * r_tow} / sum{E_tow}.
  Here r_tow=(x_tow, y_tow) denotes tower center.
• 2x1 cluster: tower energy sum for high tower plus second highest tower in 3x3 patch
• r=0.7 energy is calculated based on towers
  within a radius of 0.7 (in delta phi and eta) from high tower

Cuts applied

all gamma-jet candidate selection cuts except 3x3/r=0.7 energy isolation cut

Results

There are two sets of figures in links below:

• Number of counts for a given energy fraction
• Yield above given energy fraction
  [figures with right integral in the caption]
  

  Yield is defined as the integral above given energy fraction
  up to the maximum value of 1

Gamma candidate detector eta < 1.5
(eta region where we do have most of the TPC tracking):

1. Cluster energy fraction in 0.7 radius
2. 2x1 and 2x2 cluster energy fraction in 3x3 patch

Gamma candidate detector eta > 1.5:
(smaller tower size)

1. Cluster energy fraction in 0.7 radius
2. 2x1 and 2x2 cluster energy fraction in 3x3 patch

Some observation

• For pre1>0 condition (contains most of events)
  yield in Monte-Carlo for eta > 1.5 case
  is about factor of two different than that from pp2006 data,
  while for eta < 1.5 Monte-Carlo yield agrees with data within 10-15%.
  This could be due to trigger effect?
• For pre1=0 case yiled for both eta > 1.5 and eta < 1.5 are different in data and MC
  This could be due to migration of counts from pre1=0 to pre1>0
  in pp2006 data due to more material budget than it is Monte-Carlo
• For pre1=0 condition pp2006 data shapes are not reproduced by gamma-jet Monte-Carlo.
  With a larger cluster size (2x1 -> 3x3) the pp2006 and MC gamma-jet shapes
  are getting closer to each other.
• For pre1>0 condition (with statistics available),
  pp2006 data shapes are consistent with QCD Monte-Carlo.

 

2008.11.25 Yiled vs. analysis cuts: eta dependence

Ilya Selyuzhenkov November 25, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Some observation

• Fig. 1 [upper&lower left, 3rd bin] indicates that
  cluster energy isolation is the most important cut
  for signal/background separation
• Fig.1 [lower right, 3rd bin] shows that
  R_cluster cut is independent from (or orthogonal to) other cuts
• Fig.1 [upper&lower left 4th bin] shows that
  cut on neutral energy fraction for the away side jet
  rejects more signal that background events
  We probably need to reconsider that cut
• Fig.2 [lower left, 5th bin] shows that
  charge particle veto significantly improves
  signal to background ratio

Fig.2 [lower right, 5th bin] shows that
charge particle veto also independent from other cuts

• Fig.3 [lower left, 5th bin] shows that
  in the region were we do not have TPC tracking (photon eta > 1.5)
  charge particle veto is not efficient,
  although there is still some improvement from this cut.
  This probably due to tracks with eta <1.5
  which fall into large isolation radius r=0.7.

Yield vs. various analysis cuts

List of cuts (sorted according to bin number in Figs. 1-3. [No SMD sided residual cuts]):

1. N_events : total number of di-jet events found by the jet-finder
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy
   R_{3x3cluster}>0.9 for Fig. 1, and it is disabled in Fig. 2 and 3
4. R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet
5. N_ch=0 : no charge tracks associated with a gamma candidate
6. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
7. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
8. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
9. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
10. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster

Figure 1: Number of accepted events vs. various analysis cuts
The starting number of events (shown in first bin of the plots) is
the number of di-jets with reconstructed gamma_pt>7 GeV and jet_pt>5 GeV
upper left: cuts applied independently
upper right: expept this cut fired
(event passed all other cuts and being rejected by this cut)
lower left: "cuts applied independently" normalized by the total number of events
lower right: "expept this cut fired" normalized by the total number of events

Figure 2: Same as Fig.1 except: no R_cluster cut and photon detector eta < 1.5
(eta region where we do have most of the TPC tracking)

Figure 3: Same as Fig.1 except: no R_cluster cut and photon detector eta > 1.5

2008.12.08 Run 8 EEMC QA

Ilya Selyuzhenkov December 08, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• pp2008 - STAR 2008 pp data
  Trigger: etot-mb-l2 [id:7]
  Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Detector subsystems involved in analysis:

1. TPC (vertex, jets, charge particle veto)
2. Endcap EMC (triggering, photon candidate reconstruction)
3. Barrel EMC (away side jet reconstruction)

Gamma-jet analysis cuts:

1. Select only di-jet events
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
4. N_ch=0 : no charge tracks associated with a gamma candidate
5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Figure 1: EEMC x vs. y position of photon candidate for 2008 data sample
Problem with pre-shower layer in Sector 10 can been seen in the upper left corner

Figure 2: EEMC x vs. y position of photon candidate for 2006 data sample

Figure 3: Average < E_pre1 * E_pre2 > for 3x3 cluster around high tower
vs run number for sectors 9, 10 and 11
Note, zero pre-shower energy for sector 10 (black points) for days 61, 62, 64, and 67.
All di-jet events for pp2008 data are shown (no gamma-jet cuts)

Figure 3a: Same as Fig.3, zoom into day 61

Figure 3b: Same as Fig.3, zoom into day 62
Figure 3c: Same as Fig.3, zoom into day 64
Figure 3d: Same as Fig.3, zoom into day 67

Figure 4: EEMC x vs. y position of photon candidate for 2008 data sample
Same as Fig. 1, but excluding days: 61, 62, 64, and 67

Conclusion on QA:

No problem with pp2008 data have been found,
except that for some runs (mostly on days 61, 62, 64, and 67)
EEMC pre-shower layer for sector 10 was off.

Comparison between 2006 and 2008 data

Figure 5: Vertex z distribution:
All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67)
Results are shown for pp2008 data sample (black), vs. pp2006 data (red).
pp2008 data scaled to the same total number of candidates as in pp2006 data.

Figure 6: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 5):

Figure 7: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 5, no scaling):

2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• pp2008 - STAR 2008 pp data
  Trigger: etot-mb-l2 [id:7]
  Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

 

2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• pp2008 - STAR 2008 pp data
  Trigger: etot-mb-l2 [id:7]
  Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Gamma-jet analysis cuts:

1. Select only di-jet events
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
4. N_ch=0 : no charge tracks associated with a gamma candidate
5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Comparison between 2006 and 2008 data

Figure 1: Vertex z distribution:
All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67)
Results are shown for pp2008 data sample (black), vs. pp2006 data (red).
pp2008 data scaled to the same total number of candidates as in pp2006 data.

Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1):

Figure 3: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 1, no scaling):

2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• pp2008 - STAR 2008 pp data
  Trigger: etot-mb-l2 [id:7]
  Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Gamma-jet analysis cuts:

1. Select only di-jet events
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
4. N_ch=0 : no charge tracks associated with a gamma candidate
5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Comparison between 2006 and 2008 data

Figure 1: Vertex z distribution:
All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67)
Results are shown for pp2008 data sample (black), vs. pp2006 data (red).
pp2008 data scaled to the same total number of candidates as in pp2006 data.

Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1):

Figure 3: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 1, no scaling):

2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• pp2008 - STAR 2008 pp data
  Trigger: etot-mb-l2 [id:7]
  Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Gamma-jet analysis cuts:

1. Select only di-jet events
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
4. N_ch=0 : no charge tracks associated with a gamma candidate
5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Comparison between 2006 and 2008 data

Figure 1: Vertex z distribution:
All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67)
Results are shown for pp2008 data sample (black), vs. pp2006 data (red).
pp2008 data scaled to the same total number of candidates as in pp2006 data.

Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1):

Figure 3: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 1, no scaling):

2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• pp2008 - STAR 2008 pp data
  Trigger: etot-mb-l2 [id:7]
  Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Gamma-jet analysis cuts:

1. Select only di-jet events
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
4. N_ch=0 : no charge tracks associated with a gamma candidate
5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Comparison between 2006 and 2008 data

Figure 1: Vertex z distribution:
All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67)
Results are shown for pp2008 data sample (black), vs. pp2006 data (red).
pp2008 data scaled to the same total number of candidates as in pp2006 data.

Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1):

Figure 3: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 1, no scaling):

Conclusion:

 

2008.12.09 pp Run 8 vs. Run 6 shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• pp2008 - STAR 2008 pp data
  Trigger: etot-mb-l2 [id:7]
  Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Gamma-jet analysis cuts:

1. Select only di-jet events
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
4. N_ch=0 : no charge tracks associated with a gamma candidate
5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Comparison between 2006 and 2008 data

Figure 1: Vertex z distribution:
All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67)
Results are shown for pp2008 data sample (black), vs. pp2006 data (red).
pp2008 data scaled to the same total number of candidates as in pp2006 data.

Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1):

Figure 3: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 1, no scaling):

2008.12.11 Run 8 EEMC QA (presentation for Spin PWG)

Ilya Selyuzhenkov December 11, 2008

Run 8 QA with EEMC gamma-jet candidates

Presentation in pdf or open office file format

2008.12.16 Effect of L2gamma trigger in simulations

Ilya Selyuzhenkov December 16, 2008

Data sets

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Cuts applied

Gamma-jet isolation cuts except 3x3/r=0.7 energy isolation cut

Data driven shower shape replacement maker fix

Figure 1: (reproducing old results with dd-maker fix)
transverse momentum and vertex z distributions
before (with ideal gains/pedestals) and
after (with realistic gains/pedestal tables) dd-maker fix are in a good agreement.
For details on "dd-maker problem", read these hyper news threads:
emc2:2905, emc2:2900, and phana:294
Now we can run L2gamma trigger emulation and Eemc SMD ddMaker
in the same analysis chain.

l2-gamma trigger effect in simulation

Figure 2: Vertex z distribution with and without trigger condition in simulations
(emulated trigger: eemc-http-mb-L2gamma [id:137641]).
Solid red/green symbols show results with l2gamma condition applied,
while red/green lines show results for the same analysis cuts but without trigger condition.
Note, good agreement between MC QCD jets with trigger condition on (green solid squared)
and pp2006 data (black solid circles) for pre-shower1>0 case.

Figure 3: pt distribution with/without trigger condition in simulations.
Same color coding as in Fig. 2

Figure 4: Same as Fig. 3 just on a log scale
One can clearly see large trigger effect when applied for QCD jet events,
and a little effect for direct gammas.

Figure 5: gamma candidate pt QCD (right) and prompt photon (left) Monte-Carlo:
no (upper) with (lower) L2e-gamma trigger condition
No photon pt and no jet pt cuts

Figure 6: gamma candidate pt for QCD Monte-Carlo: no L2e-gamma trigger condition
No photon pt and no jet pt cuts

Figure 7: gamma candidate pt for QCD Monte-Carlo: L2e-gamma trigger condition applied (id:137641)
No photon pt and no jet pt cuts

2008.12.19 Parton pt distribution for Pythia QCD and gamma-jet events

Ilya Selyuzhenkov December 19, 2008

Data sets

• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Cuts applied

Gamma-jet isolation cuts except 3x3/r=0.7 energy isolation cut

Figure 1: Parton pt distibution for gamma-jet candidates from Pythia QCD sample
with various pt and l2gamma trigger conditions

Figure 2: Parton pt distibution for gamma-jet candidates from Pythia prompt photon sample
with various pt and l2gamma trigger conditions

2009.01.08 Away side jet pt vs. photon pt

Ilya Selyuzhenkov January 08, 2009

Data sets

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Cuts applied

• Di-jet events
• Require to reconstruct photon momentum (no gamma-jet isolation cuts)
• Gamma pt > 7GeV
• L2gamma emulation in Monte-Carlo
• L2gamma triggered pp2006 events

Comments

(concentrated on pre-shower1>0 case
which has better statistics for QCD Monte-Carlo):

• Fig.1, lower plots
  Vertex z distributions from QCD MC and pp2006 data are different in the negative region
• Fig.2, lower plots
  For the away side jet pt < 8GeV region
  QCD Monte-Carlo underestimates the data.
• Fig.4, lower plots
  gamma-jet pt asymmetry plot shows
  that in QCD MC photon and jet pt's are better correlated than in the data
• Fig.5, lower plots
  Most of the differences between QCD MC and pp2006 data for pre-shower1>0 case
  are probably from the lower gamma and jet pt region

Figures

Figure 1: Vertex z distribution

Figure 2: Away side jet pt

Figure 3: Photon pt

Figure 4: gamma-jet pt asymmetry: (pt_gamma - pt_jet)/pt_gamma

Figure 5: gamma pt vs. away side jet pt
1st column: triggered pp2006 data
2nd column: gamma-jet MC (l2gamma trigger on)
3rd column: QCD background MC (l2gamma trigger on)

2009.01.20 Away side jet pt vs. photon pt: more stats for QCD pt_parton 9-15GeV

Ilya Selyuzhenkov January 20, 2009

Note:
this is an update with 10x more statitstics for QCD 9-15GeV parton pt bin.
See this post for old results.

Data sets

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Cuts applied

• Di-jet events
• Require to reconstruct photon momentum (no gamma-jet isolation cuts)
• Gamma pt > 7GeV
• L2gamma emulation in Monte-Carlo
• L2gamma triggered pp2006 events

Comments

• Vertex z distributions from QCD MC and pp2006 data
  are different in the negative region (see Fig.1)
• pp2006 data to Monte-Carlo ratio
  does not depends on reconstructed photon pt,
  but it has some vertex z dependence
  (see data to MC ratio in Fig.6 for pre-shower1 > 4MeV case)

Figures

Figure 1: Vertex z distribution

Figure 2: Away side jet pt

Figure 3: Photon pt

Figure 4: gamma-jet pt asymmetry: (pt_gamma - pt_jet)/pt_gamma

Figure 5: gamma pt vs. away side jet pt
1st column: triggered pp2006 data
2nd column: gamma-jet MC (l2gamma trigger on)
3rd column: QCD background MC (l2gamma trigger on)

Data to Monte_Carlo normalization

Figure 6: pp2006 data to Monte -Carlo sum [QCD + gamma-jet] ratio
for pre-shower1>4MeV (most of statistics)
Left: data to MC ratio vs. reconstructed gamma pt.
Solid line shows constant line fit (p0 ~ 1.3)
Right: data to MC ratio vs. reconstructed vertex position

2009.01.27 gamma and jet pt plots with detector |eta|_jet < 0.8, pt_jet > 7

Ilya Selyuzhenkov January 27, 2009

Data sets

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Cuts applied

• Di-jet events
• Require to reconstruct photon momentum (no gamma-jet isolation cuts)
• jet pt > 7GeV
• Gamma pt > 7GeV or no pt cuts
• L2gamma emulation in Monte-Carlo
• L2gamma triggered pp2006 events
• cos (phi_jet - phi_gamma) < -0.8
• detector |eta_jet|< 0.8
• |v_z| < 100

Figures

All figures:

• All pre-shower conditions combined, pre1<10MeV
• Left plots: no gamma pt cut
  Right plots: pt_gamma >7GeV
• Thick blue line shows MC sum: QCD + gamma-jet
• Thin solid color lines shows distributions from various partonic pt bins for QCD MC
  See figures legend for color coding

Figure 1: Vertex z distribution

Figure 2: Photon eta

Figure 3: Away side jet eta

Figure 4:Photon pt

Figure 5: Away side jet pt

Figure 6: Away side jet detector eta

2009.01.27 gamma and jet pt plots with |eta|_jet < 0.7

Ilya Selyuzhenkov January 27, 2009

Data sets

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Cuts applied

• Di-jet events
• Require to reconstruct photon momentum (no gamma-jet isolation cuts)
• Gamma pt > 7GeV or no pt cuts
• L2gamma emulation in Monte-Carlo
• L2gamma triggered pp2006 events
• cos (phi_jet - phi_gamma) < -0.8
• |eta_jet|< 0.7
• |v_z| < 100

Figures

All figures:

• All pre-shower conditions combined, pre1<10MeV
• Left plots: no gamma pt cut
  Right plots: pt_gamma >7GeV
• Thick blue line shows MC sum: QCD + gamma-jet
• Thin solid color lines shows distributions from various partonic pt bins for QCD MC
  See figures legend for color coding

Figure 1: Vertex z distribution

Figure 2: Photon eta

Figure 3: Away side jet eta

Figure 4:Photon pt

Same as in Fig.4 on a log scale: no gamma pt cut and pt>7GeV

Figure 5: Away side jet pt

Same as in Fig.5 on a log scale: no gamma pt cut and pt>7GeV

2009.02.02 No pre-shower cuts, Normalization fudge factor 1.24

Ilya Selyuzhenkov February 02, 2009

Data sets

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Cuts applied

• Di-jet events
• Require to reconstruct photon momentum (no gamma-jet isolation cuts)
• Gamma pt > 7GeV
• L2gamma emulation in Monte-Carlo
• L2gamma triggered pp2006 events
• cos (phi_jet - phi_gamma) < -0.8
• detector |eta_jet|< 0.8
• |v_z| < 100

Figures

All figures:

• All pre-shower conditions combined, No pre-shower cuts
• Thick blue line shows MC sum: QCD + gamma-jet
• Black solid circles: pp2006 data
• Monte-Carlo results first scaled to 3.164 pb^-1 according to Pythia luminosity
  and then an additional fudge factor of 1.24 has been applied.
  Fudge factor is defined as the yields ratio from data to scaled with Pythia luminosity Monte-Carlo
  for pt_jet>7GeV and pt_gamma>7 candidates

Figure 1: Vertex z distribution with pt_jet>7 cut (left) and without pt_jet cut (rigth)

Figure 2: Photon (left) and away side jet (right) pt

Figure 3: Photon detector eta (left) and corrected for vertex eta (right)

Figure 4: Away side jet detector eta (left) and corrected for vertex eta (right)

Figure 5: Preshower 1 (left) and Pre-shower2 (right) energy

2009.02.03 No pre-shower cuts, pt_jet >7 vs. No pt_jet cuts

Ilya Selyuzhenkov February 03, 2009

Data sets

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.
• QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
  Partonic pt range 2-25 GeV.

Cuts applied

• Di-jet events
• Require to reconstruct photon momentum (no gamma-jet isolation cuts)
• Gamma pt > 7GeV
• L2gamma emulation in Monte-Carlo
• L2gamma triggered pp2006 events
• cos (phi_jet - phi_gamma) < -0.8
• detector |eta_jet|< 0.8
• |v_z| < 100

Figures

Each figure has:

• All pre-shower conditions combined, No pre-shower cuts
• Thick blue line shows MC sum: QCD + gamma-jet
• Black solid circles shows pp2006 data
• Left plots: pt_jet>7GeV
  Right plots: no cuts on pt_jet
• Monte-Carlo results for QCD and gamma-jet samples are first
  scaled to 3.164 pb^-1 according to Pythia luminosity,
  added together, and then an additional fudge factor of 1.24 applied.
  Fudge factor is defined as pp2006 to Monte-Carlo sum ratio
  for pt_jet>7GeV and pt_gamma>7 candidates

Figure 1: Vertex z distribution

Figure 2: Photon detector eta

Figure 3: Corrected for vetrex photon eta

Figure 4: Away side jet detector eta

Figure 5: Corrected for vetrex away side jet eta

Figure 6:Photon pt

Figure 7: Away side jet pt

Figure 8: Pre-shower 1 energy

Figure 9: Pre-shower 2 energy

2009.02.06 Pre-shower energy distribution Run6 vs. Run8 geometry

Ilya Selyuzhenkov February 06, 2009

Data sets

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• mc2006: gamma-jet+QCD jets [p6410EemcGammaFilter] events.

Partonic pt range 2-25 GeV.

• pp2008 - STAR 2008 pp data
  Trigger: etot-mb-l2 [id:7]

Cuts applied

• Di-jet events
• Require to reconstruct photon momentum (no gamma-jet isolation cuts)
• Gamma pt > 7GeV, jet pt > 7GeV
• L2gamma emulation in Monte-Carlo
• L2gamma triggered for pp2006 and pp2008 events
• cos (phi_jet - phi_gamma) < -0.8
• detector |eta_jet|< 0.8
• |v_z| < 100

Figures

Each figure has:

• All pre-shower conditions combined, No pre-shower cuts
• Red circles show pp2006 data
• Black triangles show pp2008 data
  Data scaled to match the integraled yield from pp2006 data
• Green line shows MC sum: QCD + gamma-jet
  Monte-Carlo results for QCD and gamma-jet samples are first
  scaled to 3.164 pb^-1 according to Pythia luminosity,
  added together, and then an additional fudge factor of 1.24 applied.
  Fudge factor is defined as pp2006 to Monte-Carlo sum ratio
  for pt_jet>7GeV and pt_gamma>7 candidates

Observations

• Pre-shower energy distributions from pp2008 data set
  are narrower than that for pp2006 data.
  This corresponds to smaller amount of material budget in y2008 STAR geometry.
• Pre-shower energy distribution from Monte-Carlo with y2006 geometry
  closer follows the distribution from pp2008 data set, rather than that from pp2006 data.
  This indicates the lack of material budget in y2006 Monte-Carlo.

Note: There is a "pre-shower sector 10 problem" for pp2008 data,
which results in migration of small fraction of events with pre-shower>0 into
pre-shower=0 bin (first zero bins in Fig.1 and 2. below).
For pre-shower>0 case this only affects overall normalization of pp2008 data,
but not the shape of pre-shower energy distributions.
I'm running jet-finder+my software to get more statistics from pp2008 data set,
and after more QA will produce list of runs with "pre-shower sector 10 problem",
so to exclude them in the next iteration of my plots.

Figure 1: Pre-shower1 energy distribution

Figure 2: Pre-shower2 energy distribution

2009.02.09 pp2006, pp2008, amd mc2006 comparison

Ilya Selyuzhenkov February 06, 2009

Data sets

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• mc2006: gamma-jet+QCD jets [p6410EemcGammaFilter] events.

Partonic pt range 2-25 GeV.

• pp2008 - STAR 2008 pp data
  Trigger: etot-mb-l2 [id:7]

Cuts applied

• Di-jet events
• Require to reconstruct photon momentum (no gamma-jet isolation cuts)
• Gamma pt > 7GeV, jet pt > 7GeV
• L2gamma emulation in Monte-Carlo
• L2gamma triggered for pp2006 and pp2008 events
• cos (phi_jet - phi_gamma) < -0.8
• detector |eta_jet|< 0.8
• |v_z| < 100

Figures

Each figure has:

• All pre-shower conditions combined, No pre-shower cuts
• Red circles show pp2006 data
• Black triangles show pp2008 data
  Data scaled to match the integraled yield from pp2006 data
• Green line shows MC sum: QCD + gamma-jet
  Monte-Carlo results for QCD and gamma-jet samples are first
  scaled to 3.164 pb^-1 according to Pythia luminosity,
  added together, and then an additional fudge factor of 1.24 applied.
  Fudge factor is defined as pp2006 to Monte-Carlo sum ratio
  for pt_jet>7GeV and pt_gamma>7 candidates

Kinematics

Figure 1: vertex z

Figure 2: photon detector eta

Figure 3: jet detector eta

Figure 4: photon pt

Figure 5: jet pt

Figure 6: gamma-jet pt balance

Figure 7: Photon neutral energy fraction

Figure 8: Jet neutral energy fraction

Figure 9: cos(phi_gamma-phi_jet)

Photon candidate's 2x1, 2x2, and 3x3 tower cluser energy

Figure 10: 3x3 cluster energy

Figure 11: 2x1 cluster energy

Figure 12: 2x2 cluster energy

Number of charge tracks, Barrel and Endcap towers within r=0.7 for photon and gamma

Figure 13: Number of charged track associated with photon candidate

Figure 14: Number of Barrel towers associated with photon candidate

Figure 15: Number of Endcap towers associated with photon candidate

Jet energy composition

Figure 16: Jet energy part from Barrel towers

Figure 17: Jet energy part from charge tracks

2009.02.16 pt_jet>5GeV: pre-shower sorting with new normalization

Ilya Selyuzhenkov February 16, 2009

Data sets

• pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
  Trigger: eemc-http-mb-L2gamma [id:137641]
• mc2006: gamma-jet+QCD jets [p6410EemcGammaFilter] events.

Partonic pt range 2-25 GeV.

• pp2008 - STAR 2008 pp data
  Trigger: etot-mb-l2 [id:7]

Cuts applied

• Di-jet events
• Require to reconstruct photon momentum (no gamma-jet isolation cuts)
• Gamma pt > 7GeV, jet pt > 7GeV
• L2gamma emulation in Monte-Carlo
• L2gamma triggered for pp2006 and pp2008 events
• cos (phi_jet - phi_gamma) < -0.8
• detector |eta_jet|< 0.8
• |v_z| < 100

Figures

Each figure has:

• pp2008 data scaled to match the integraled yield from pp2006 data
• mc2006 stand for MC sum: QCD + gamma-jet
  Monte-Carlo results for QCD and gamma-jet samples are first
  scaled to 3.164 pb^-1 according to Pythia luminosity,
  added together, and then an additional fudge factor of 1.24 applied.
  Fudge factor is defined as pp2006 to Monte-Carlo sum ratio
  for pt_jet>7GeV and pt_gamma>7 candidates

plots for pt_gamma>7GeV, pt_jet > 5GeV

1. All pre-shower combined: 1D distributions
2. All pre-shower combined: 2D correlations
3. Pre-shower sorting 1D distributions

 

2009.02.19 Photon-jet analysis status update for Spin PWG

Photon-jet analysis status update for Spin PWG (February 19, 2009)

Slides: download pdf

Previous versions: v1, v2

Link for CIPANP abstract

 

 

CIPANP 2009 abstract on photon-jet measurement

CIPANP 2009 abstract on photon-jet study

Title:
"Photon-jet coincidence measurements
in polarized pp collisions at sqrt{s}=200GeV
with the STAR Endcap Calorimeter"

Abstract: download pdf

Previous versions: v1, v2, v3, v4

Conference link: CIPANP 2009

2009.03.02 Application of the neural network for the cut optimization (zero try)

Multilayer perceptron (feedforward neural networks)

Multilayer perceptron (MLP) is feedforward neural networks
trained with the standard backpropagation algorithm.
They are supervised networks so they require a desired response to be trained.
They learn how to transform input data into a desired response,
so they are widely used for pattern classification.
With one or two hidden layers, they can approximate virtually any input-output map.
They have been shown to approximate the performance of optimal statistical classifiers in difficult problems.

ROOT implementation for Multilayer perceptron

TMultiLayerPerceptron class in ROOT
mlpHiggs.C example

Application for cuts optimization in the gamma-jet analysis

Netwrok structure:
r3x3, (pt_gamma-pt_jet)/pt_gamma, nCharge, bBtow, eTow2x1: 10 hidden layers: one output later

Figure 1:

• Upper left: Learning curve (error vs. number of training)
  Learing method is: Steepest descent with fixed step size (batch learning)
• Upper right: Differences (how important are initial variableles for signal/background separation)
• Lower left: Network structure (ling thinkness corresponds to relative weight value)
• Lower right: Network output. Red - MC gamma-jets, blue QCD background, black pp2006 data

 

Figure 2: Input parameters vs. network output
Row: 1: MC QCD, 2: gamma-jet, 3 pp2006 data
Vertical axis: r3x3, (pt_gamma-pt_jet)/pt_gamma, nCharge, bBtow, eTow2x1
Horisontal axis: network output

Figure 3: Same as Fig. 2 on a linear scale

2009.03.09 Application of the LDA and MLP classifiers for the cut optimization

Cut optimization with Fisher's LDA and MLP (neural network) classifiers

ROOT implementation for LDA and MLP:

• Fisher's Linear Discriminant Analysis (LDA)
• Multi-Layer Perceptron (MLP) [feed-forward neural network]

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

MLP configuration:

• 2 hidden layers [N+1:N neural network configuration, N is number of input parameters]
• Learning method: stochastic minimization (1000 learning cycles)

Input parameters (same for both LDA and MLP):

1. Energy fraction in 3x3 cluster within a r=0.7 radius: r3x3
2. Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma
3. Number of charge tracks within r=0.7 around gamma candidate
4. Number of Endcap towers fired within r=0.7 around gamma candidate
5. Number of Barrel towers fired within r=0.7 around gamma candidate

Figure 1: Signal efficiency and purity, background rejection (left),
and significance: Sig/sqrt[Sig+Bg] (right) vs. LDA (upper plots) and MLP (lower plots) classifier discriminants

Figure 2:

1. Upper left: Rejection vs. efficiency
2. Upper right: Purity vs. efficiency
3. Lower left: Purity vs. Rejection
4. Lower right: Significance vs. efficiency

 

Figure 3: Data to Monte-Carlo comparison for LDA (upper plots) and MLP (lower plots)
Good (within ~ 10%) match between data nad Monte-Carlo
a) up to 0.8 for LDA discriminant, and b) up to -0.7 for MLP.

Figure 4: Data to Monte-Carlo comparison for input parameters
from left to right
1) pt_gamma 2) pt_jet 3) r3x3 4) gamma-jet pt balance 5) N_ch[gamma] 6) N_eTow[gamma] 7) N_bTow[gamma]
Colour coding: black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD

Figure 5: Data to Monte-Carlo comparison:
correlations between input variables (in the same order as in Fig. 4)
and LDA classifier discriminant (horizontal axis).
1st raw: QCD MC; 2nd: gamma-jet MC; 3rd: pp2006 data; 4th: QCD+gamma-jet MC

Figure 6: Same as Fig. 6 for MLP discriminant

2009.03.26 Endcap photon-jet update at the STAR Collaboration meeting

Endcap photon-jet update at the STAR Collaboration meeting

2009.04.17 WSU nuclear seminar

The STAR spin program with longitudinally polarized proton beams

2009.04.21 Adding SMD info to the LDA

Cut optimization with Fisher's LDA classifier

ROOT implementation for LDA:

• Fisher's Linear Discriminant Analysis (LDA)

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

LDA input parameters:

1. Energy fraction in 3x3 cluster within a r=0.7 radius: r3x3
2. Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma
3. Number of charge tracks within r=0.7 around gamma candidate
4. Number of Endcap towers fired within r=0.7 around gamma candidate
5. Number of Barrel towers fired within r=0.7 around gamma candidate

Figure 1: LDA discriminant (no SMD involved in training)

Figure 2: LDA (no SMD): Efficiency, rejection, purity vs. discriminant

Figure 3: SMD energy in 25 central strips (LDA-dsicriminant>0, no pre-shower1 cut)

Figure 4: SMD energy in 25 central strips (LDA-dsicriminant>0, pre-shower1 < 10MeV)

Figure 5: Maximum residual (LDA-dsicriminant>0, no pre-shower1 cut)

Figure 6: Maximum residual (LDA-dsicriminant>0, pre-shower1 < 10MeV)

LDA+ SMD analysis

SMD info added:
a) energy in 5 central srtips
b) maximum sided residual

Figure 7:LDA with SMD: Efficiency, rejection, purity vs. LDA discriminant

Figure 8: LDA discriminant with SMD

Figure 9: Maximum residual (SMD LDA-dsicriminant>0, pre-shower1 < 10MeV)

LDA with and without SMD comparison

Figure 10:LDA (no SMD): Efficiency, rejection, purity plots

Figure 11: LDA with SMD: Efficiency, rejection, purity plots

2009.04.28 LDA plus SMD analysis with pre-shower sorting

Cut optimization with Fisher's LDA classifier

ROOT implementation for LDA:

• Fisher's Linear Discriminant Analysis (LDA)

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

LDA input parameters (includes SMD inromation of the distance from max sided residual plot):

1. Energy fraction in 3x3 cluster within a r=0.7 radius: r3x3
2. Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma
3. Number of charge tracks within r=0.7 around gamma candidate
4. Number of Endcap towers fired within r=0.7 around gamma candidate
5. Number of Barrel towers fired within r=0.7 around gamma candidate
6. Distance to 80% cut line (see this link for more details)

The number of strips in SMD u or v planes is required to be greater than 3

Figure 1: SMD energy in 25 central strips sorted by pre-shower energy

1. Upper left: pre1=0, pre2=0
2. Upper right: pre1=0, pre2>0
3. Lower left: 0<4MeV
4. Lower right: 4<10MeV

Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samples
Colour coding:
black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD
(combined plot for all pre-shoer bins can be found here)

 

Figure 2: SMD energy in 5 central strips sorted by pre-shower energy
(combined plot can be found here)

Figure 3: Maximum residual sorted by pre-shower energy
(combined plot can be found here)

Figure 4: LDA discriminant. Note: LDA algo trained for each pre-shower condition independently

Figure 5: LDA: Efficiency, rejection, purity vs. discriminant, sorted by pre-shower energy

Figure 6: LDA: Efficiency, rejection, purity plots sorted by pre-shower energy
For each pre-shower condition each plot has 4 figures:

1. u-left: rejection vs. efficiency
2. u-right: purity vs. efficiency
3. l-left: purity vs. rejection
4. l-right: significance (signal/sqrt{signal+background}) vs. efficiency

 

2009.05.03 LDA: varying pt and eta cut

Cut optimization with Fisher's LDA classifier

ROOT implementation for LDA:

• Fisher's Linear Discriminant Analysis (LDA)

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

LDA input parameters Set0:

1. Set0:
   • Energy fraction in 3x3 cluster within a r=0.7 radius:
     E_3x3/E_0.7
   • Photon-jet pt balance:
     [pt_gamma-pt_jet]/pt_gamma
   • Number of charge tracks within r=0.7 around gamma candidate:
     Ncharge
   • Number of Endcap towersL fired within r=0.7 around gamma candidate:
     NtowBarrel
   • Number of Barrel towers fired within r=0.7 around gamma candidate
     NtowEndcap
2. Set1:
   • All from Set0
   • Shower shape analysis: distance to 80% cut line:
     distance to cut line
3. Set2:
   • All from Set1
   • Energy fraction in E_2x1 and E_2x2 witin E_3x3:
     E_2x1/E_2x2 and E_2x2/E_3x3
4. Set3:
   • All from Set2
   • Energy in post-shower layer under 3x3 tower patch:
     E_post^3x3

The number of strips in SMD u or v planes is required to be greater than 3

Pre-shower sorting (energy in tiles under 3x3 tower patch):

1. pre1=0, pre2=0
2. pre1=0, pre2>0
3. 0 < pre1 < 0.004
4. 0.004 < pre1 < 0.01
5. pre1 < 0.01
6. pre1 >= 0.01

Photon pt and rapidity cuts:

1. pt>7GeV
2. pt>8GeV
3. pt>9GeV
4. pt>10GeV
5. detector eta <1.4 (pt>7GeV)
6. detector eta > 1.4 (pt>7GeV)

Figure 0: photon pt distribution for pre-shower1<0.01
Colour coding:
black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD

LDA Set0

Figure 1: LDA discriminant with Set0: Data to Monte-Carlo comparison (pt>7GeV cut)

Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samples
Colour coding:
black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD

Figure 2: efficiency, purity, rejection vs. LDA discriminant (pt>7GeV cut)

Figure 3: rejection vs. efficiency

Figure 4: purity vs. efficiency

Figure 5: purity vs. rejection

LDA Set1

Figure 6: LDA discriminant with Set1: Data to Monte-Carlo comparison

Figure 7: rejection vs. efficiency

Figure 8: purity vs. efficiency

Figure 9: purity vs. rejection (click link to see the figure)

LDA Set2

Figure 10: rejection vs. efficiency (click link to see the figure)

Figure 11: purity vs. efficiency

Figure 12: purity vs. rejection (click link to see the figure)

LDA Set3

Figure 13: rejection vs. efficiency (click link to see the figure)

Figure 14: purity vs. efficiency

Figure 15: purity vs. rejection (click link to see the figure)

2009.05.04 LDA: More SMD info, 3x3 tower energy, correlation matrix

Cut optimization with Fisher's LDA classifier

ROOT implementation for LDA:

• Fisher's Linear Discriminant Analysis (LDA)

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

LDA input parameters Set0:

1. Set4 (link for results with LDA Set0-Set3):
   • Energy fraction in 3x3 cluster within a r=0.7 radius:
     E_3x3/E_0.7
   • Photon-jet pt balance:
     [pt_gamma-pt_jet]/pt_gamma
   • Number of charge tracks within r=0.7 around gamma candidate:
     Ncharge
   • Number of Endcap towersL fired within r=0.7 around gamma candidate:
     NtowBarrel
   • Number of Barrel towers fired within r=0.7 around gamma candidate
     NtowEndcap
   • Shower shape analysis: distance to 80% cut line:
     distance to cut line
   • Energy fraction in E_2x1 and E_2x2 witin E_3x3:
     E_2x1/E_2x2 and E_2x2/E_3x3
   • Energy in post-shower layer under 3x3 tower patch:
     E_post^3x3
   • Tower energy in 3x3 patch:
     E_tow^3x3
   • SMD-u energy in 25 central strips:
     E_smd-u^25
   • SMD-v energy in 25 central strips:
     E_smd-v^25
   • SMD-v peak energy (in 5 central strips):
     E_peak

The number of strips in SMD u or v planes is required to be greater than 3

Pre-shower sorting (energy in tiles under 3x3 tower patch):

1. pre1=0, pre2=0
2. pre1=0, pre2>0
3. 0 < pre1 < 0.004
4. 0.004 < pre1 < 0.01
5. pre1 < 0.01
6. pre1 >= 0.01

Integrated yields per pre-shower bin:

sample	total integral	pre1=0,pre2=0	pre1=0, pre2>0	0 < pre1 < 0.004	0.004 < pre1 < 0.01	pre1 < 0.01	pre1 >= 0.01
photon-jet	2.5640e+03	3.5034e+02	5.2041e+02	5.6741e+02	5.2619e+02	1.9644e+03	5.9994e+02
QCD	5.6345e+04	1.3515e+03	4.3010e+03	1.2289e+04	1.5759e+04	3.3701e+04	2.2644e+04
pp2006	6.2811e+04	6.8000e+02	2.4310e+03	1.2195e+04	1.6766e+04	3.2072e+04	3.0739e+04

Photon pt and rapidity cuts:

1. pt>7GeV
2. pt>8GeV
3. pt>9GeV
4. pt>10GeV
5. detector eta <1.4 (pt>7GeV)
6. detector eta > 1.4 (pt>7GeV)

LDA Set4

Figure 1: LDA discriminant with Set0: Data to Monte-Carlo comparison (pt>7GeV cut)

Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samples
Colour coding:
black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD

Figure 2: rejection vs. efficiency

Figure 3: purity vs. efficiency

Figure 4: purity vs. rejection

Figure 5: Correlation matrix (pt>7GeV cut)
pre1=0, pre2=0

pre1=0, pre2>0

0 < pre1 < 0.004

0.004 < pre1 < 0.01

pre1 < 0.01

pre1 >= 0.01

2009.05.06 Applying cuts on LDA: request minimum purity or efficiency

Cut optimization with Fisher's LDA classifier

For this post LDA input parameters Set4 has been used

LDA for various pre-shower bins is trained independetly,
and later results with pre-shower1<0.01 are combined.

There are a set of plots for various photon pt cuts (pt> 7, 8, 9 10 GeV)
and with different selection of cutoff for LDA
(either based on purity or efficiency).
Number in brackets shows the total yield for the sample.

Link to all plots (16 total) as a single pdf file

pt > 7GeV

Figure 1: pt > 7GeV, efficiency@70

Figure 2: pt > 7GeV, purity@35

 

Figure 3: pt > 7GeV, purity@40

 

Figure 4: pt > 7GeV, purity@25 (Note: very similar to results with efficiency@70)

 

pt > 9GeV

Figure 5: pt > 9GeV, efficiency@70

 

Figure 6: pt > 9GeV, purity@35

 

pt > 10GeV

Figure 7: pt > 10GeV, efficiency@70

 

Figure 8: pt > 10GeV, purity@40

 

2009.05.07 Photon-jets analysis with the Endcap Calorimeter

Photon-jets with the Endcap Calorimeter

(analysis status update for Spin PWG)

Slides in pdf format:

• Version 2
• Version 1

 

2009.05.12 Variable distributions after LDA at 70% efficiency

Cut optimization with Fisher's LDA classifier

For this post LDA results with Set1 and Set2 has been used
Note, that LDA for various pre-shower bins is trained independetly

pdf-links with results for pre1=0 and pre2=0 (pre-shower bin 1):

• before LDA cut (figures are not sorted)
• after LDA cut @70% efficiency for Set 2 (figures are not sorted)

Figures below are for 0.004<pre-shower1<0.01 (pre-shower bin 4).

Photon pt cut: pt> 7, pre-shower bin: 0.004 < pre1 < 0.01
LDA cut with efficiency @ 70%

Set1 vs. Set2

What is added in Set2 compared to Set1:
smaller cluster size information (r2x1, r2x2), post-shower energy

Figure 1: r2x1
before LDA cut

LDA cut for Set1

LDA cut for Set2

Figure 2: r2x2
before LDA cut

LDA cut for Set1

LDA cut for Set2

Figure 3: r3x3
before LDA cut

LDA cut for Set1

LDA cut for Set2

Figure 4: Residual distance
before LDA cut

LDA cut for Set1

LDA cut for Set2

Other variables with LDA Set2 cut

Note: Only plos for LDA cut @70 efficiency for Set2 are shown

Figure : number of charge particles around photon

 

Figure 5: number of EEMC tower around photon

 

Figure 6: number of BEMC tower around photon

 

Figure 7: photon-jet pt balance

 

Figure 8: SMD energy in 5 centrapl strips

 

Figure 9: SMD energy in 25 central strips: u and v plane separately (plot for V plane)

 

Figure 10: 2x1 cluster energy

 

Figure 11: 2x2 cluster energy

 

Figure 12: 3x3 cluster energy

 

Figure 13: tower energy in r=0.7 radius

 

Figure 14: 3x3 pre-shower1 energy

 

Figure 15: 3x3 pre-shower2 energy

 

Figure 16: 3x3 post-shower energy

 

Figure 17: photon pt

 

Figure 18: jet pt

 

Figure 19: z vertex

2009.05.31 CIPANP 2009 photon-jet presentation

CIPANP 2009 presentation on photon-jet study

Title:
"Photon-jet coincidence measurements
in polarized pp collisions at sqrt{s}=200GeV
with the STAR Endcap Calorimeter"

• Presentation in pdf format
• Older versions: 0, 1, 2 3, 4, 5, open office file: odp
• Submitted abstract
• Conference link: CIPANP 2009

2009.06.22 CIPANP 2009 photon-jet proceedings

CIPANP 2009 proceedings on photon-jet study

Title:
"Photon-jet coincidence measurements
in polarized pp collisions at sqrt{s}=200 GeV
with the STAR Endcap Calorimeter"

• Proceedings: latest version
  Older versions: 01 02 03 04 05
• Presentation
• Submitted abstract
• Conference link: CIPANP 2009

2009.07.21 EEMC tower response in Monte-Carlo

Data set and cuts:

1. gamma-jet filtered Monte-Carlo
2. Di-jet events from the jet finder (jets threshold: 3.5 GeV)
3. parton pt bin 3-4 GeV (see pt_gamma distributions for various parton pt bins)
4. Thrown photon pseudo-rapidity: eta in [1-2] range
5. Requires to reconstruct photon candidate in the EEMC

Figure 1: Average ratio: pt_true / (pt_reco/1.3) vs. pt_reco (GeV/c)

• Introduce 1.3 factor here to remove the effect of the fudge factor in slow simulator
• Since a limited partonic pt range (3-4 GeV) is used for this study,
  there is an "artificial" increase of the plotted ratio in pt_gamma > 6 GeV range
• Fig. 1 reflects similar features (over a limited pt range) as those found by Hal
  in his single photon study (see slide 6 of SimulationStudies.ppt presentation)

 

Figure 2:
Average momentum difference: pt_true - (pt_reco/1.3) vs. pt_reco (GeV/c)

• Fig. 2 shows that on average in GEANT Monte-Carlo we miss ~1GeV independent on the photon pt.
  EEMC detector response can be still linear even if the ratio in Fig. 1 is not flat.
• Usage of fixed 1.3 (or others, like 1.25) fudge factors are not justified.
• It seems that using pt-dependent fudge factor (like it is done in this Jason's study)
  is also unjustified, since the same effects (flat ratio of pt_reco/pt_true ~ 1)
  can be reached by subtracting 1 GeV from the cluster energy (See Fig. 3).

 

Figure 3: Average ratio: (pt_true -1.06) (pt_reco/1.3) vs. pt_reco (GeV/c)
Similar to Fig. 1, but with the true photon pt reduced by 1.06 GeV
Resulting true/reco pt ratio is flat in 4-6 GeV range.

Before further pursuing our efforts in tuning the tower energy response in the Monte-Carlo,
needs to address the observed energy loss difference in the fisrt layer of the BEMC/EEMC detector.
See Jason's blog post from 2009.07.16 for more details:
Comparison muon energy deposit in the 1st BEMC/EEMC layers

2009.08.24 Test of corrected EEMC geometry

Test of corrected EEMC geometry (bug 1618)

Monte-Carlo setup:

• One particle per event (photons, electrons, and pions)
• Full STAR 2006 geometry.
  In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so
• Flat in eta (1.08-2.0), phi (0,2pi), and pt (3-30 GeV)
• Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
  what assumes fixed sampling fraction of 0.05 (5%)

Some definitions:

• Et correction factor : average p_T^thrown / E_T^{reco}.
  E_T^{reco} is the total energy in the Endcap Calorimeter (from A2Emaker)
• Sampling fraction: average 0.05 * Energy^{reco} / Energy^thrown.
• SMD energy: average energy in all strips fired (u-plane used for this post)
• Number of SMD strips fired: average total number of strips fired (u-plane used for this post)

Notations used in the plots:

• Left plots: no cAir fix
• Right plots: cAir-fixed
• Photons: black
• Electrons: red
• Pions: green

Et correction

Note: compare "Left" plots with Brians old results

Figure 1a: Et correction factor vs. pt thrown

Figure 1b: Et correction factor vs. eta thrown

Figure 1c: Et correction factor vs. phi thrown

Sampling fraction

Note: compare "Right" plots with Jason results with EEMC only geometry

Figure 2a: Sampling fraction vs. pt thrown

Figure 2b: Sampling fraction vs. energy thrown

Figure 2c: Sampling fraction vs. eta thrown

Figure 2d: Sampling fraction vs. phi thrown

SMD energy

Figure 3a: SMD energy vs. energy thrown

Figure 3b: SMD energy vs. eta thrown

Number of SMD strips fired

Figure 4a: Number of SMD strips fired vs. energy thrown

Figure 4b: Number of SMD strips fired vs. eta thrown

2009.08.25 Test of corrected EEMC geometry: shower shapes

Test of corrected EEMC geometry (bug 1618)

Monte-Carlo setup is desribed here

• One particle per event (photons, electrons, and pions)
• Full STAR 2006 geometry.
  In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so
• Flat in eta (1.08-2.0), phi (0,2pi), and pt (3-30 GeV)
• Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
  what assumes fixed sampling fraction of 0.05 (5%)
• Vertex z=0
• ~50K/per particle type
• Non-zero energy: 3 sigma above pedestal

Figure 1:Single photon shower shape before (red) and after (black) EEMC cAir bug fixed
pt=7-8GeV, eta=1.2-1.4 (left), eta=1.6-1.8 (right)

Figure 2: Single photon shower shape vs. data
Monte-Carlo: pt=7-10GeV, eta=1.6-1.8
data: no pre-shower1,2; pt_photon>7, pt_jet>5. no eta cuts.
(see Fig. 1 from here for other pre-shower conditions)

2009.08.27 fixed EEMC geometry: pre-shower sorted shower shapes & eta-meson comparison

Test of corrected EEMC geometry: shower shapes (bug 1618)

Monte-Carlo setup is desribed here

• One particle per event (photons, electrons, and pions)
• Full STAR 2006 geometry.
  In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so
• Flat in eta (1.08-2.0), phi (0,2pi), and pt (3-30 GeV)
• Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
  what assumes fixed sampling fraction of 0.05 (5%)
• Vertex z=0
• ~50K/per particle type
• Non-zero energy: 3 sigma above pedestal

Color coding:

• Black - photon (single particle/event MC)
• Red - electron (single particle/event MC)
• Green - neutral pion (single particle/event MC)
• Blue - photons from eta-meson decay (real data)

Single particle shower shape before (left) and after (right) EEMC cAir bug fixed
Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip).

Figure 1: Pre-shower bin 0: E_pre1=0; E_pre2=0

Figure 2: Pre-shower bin 1: E_pre1=0; E_pre2>0

Figure 3: Pre-shower bin 2: E_pre1>0; E_pre1<0.004

Figure 4: Pre-shower bin 3: E_pre1>0.004; E_pre1<0.01

Shower shape ratios

Results only for corrected EEMC geometry
All shapes are divided by MC single-photon shower shape.

Figure 5a: Pre-shower bin 0: E_pre1=0; E_pre2=0

Figure 5b: Pre-shower bin 1: E_pre1=0; E_pre2>0

Figure 5c: Pre-shower bin 2: E_pre1>0; E_pre1<0.004

Figure 5d: Pre-shower bin 3: E_pre1>0.004; E_pre1<0.01

Figure 6: Single photon to eta-meson shape ratios only (with error bars):
Pre-shower bins 0 (upper-left),1 (upper-right),2 (lower-left), and 3 (lower-right)

Extracting gamma-jet cross section at forward rapidity from pp@200GeV collisions

Analysis overview

1. Data samples, event selection, luminosity determination
2. Isolating photon-jet events
   • Transverse shower shape analysis
   • Isolation cuts
   • Cut optimization
3. Trigger effects study
4. Data to Monte-Carlo comparison/normalization and raw yields
5. Acceptance/efficiency corrections
6. Corrected yields
7. Background subtraction
8. Systematic uncertainties
9. Comparison with theory

Data samples, event selection, luminosity determination

Real data, and signal/background Monte-Carlo samples:

• pp@200GeV collisions, STAR produnctionLong.
  Trigger: eemc-http-mb-L2gamma [id:137641] (L ~ 3.164 pb^1)

• Pythia prompt photon (signal) Monte-Carlo sample.
  Filtered Prompt Photon p6410EemcGammaFilter.
  Partonic pt range 2-25 GeV.

• Pythia 2->2 hard QCD processes (background) Monte-Carlo sample.
  Filtered QCD p6410EemcGammaFilter.
  Partonic pt range 2-25 GeV.

Isolating photon-jet events

1. Shower shape analysis
   • Shower shapes
   • Sided residual analysis
2. Isolation cuts
   • Energy clustering (2x1 vs. 2x2 vs. 3x3, cluster energy vs. rapidity)
   • Charge particle veto
   • photon and jet pt balance
   • Tower multiplicity
3. Cut optimization with LDA.
   Input variables (list can be expanded):
   • Energy fraction in 3x3 cluster within a r=0.7 radius, E_3x3/E_0.7
   • Photon-jet pt balance, [pt_gamma-pt_jet]/pt_gamma
   • Number of charge tracks within r=0.7 around gamma candidate
   • Number of Endcap towers fired within r=0.7 around gamma candidate
   • Number of Barrel towers fired within r=0.7 around gamma candidate
   • Shower shape analysis: distance to 80% cut line
   • Energy fraction in E_2x1 and E_2x2 witin E_3x3
   • Energy in post-shower layer under 3x3 tower patch
   • Tower energy in 3x3 patch
   • SMD-u/v energy in 25 central strips
   • SMD-u/v peak energy (in 5 central strips)

Trigger effects study

No studies yet

• Trigger effects vs pt
• Trigger effects vs eta
• What else?

Data to Monte-Carlo comparison/normalization and raw yields

• Overall data to MC normalization based on vertex z distribution
• Data to MC comparison of raw yield in various detector subsystems
• Uncorrected yields optimized with different efficiency/purity

Acceptance/efficiency corrections

No studies yet

• What needs to be studied for acceptance/efficiency effects?
• Converting reconstructed photon (jet) energy/momentum to the true one
• Reconstruction efficiency vs. rapidity, pt, etc

Corrected yields

No studies yet

• Produce acceptance/efficiency corrected yields

Background subtraction

No studies yet

• Statistical background subtraction based on Pythia+GEANT Monte-Carlo
• Estimate systematic uncertainties due to background subtraction

Systematic uncertainties

No studies yet

• Calorimeter energy resolution
• Trigger bias
• Other effects

Comparison with theory

No comparison yet

• Request for pQCD calculations at forward rapidity

2009.09.04 Test of corrected EEMC geometry: SVT, slow-simulator on/off, pre-shower migration

Test of corrected EEMC geometry: shower shapes (bug 1618)

Monte-Carlo setup:

• One particle per event (photons, electrons, pions, and eta-meson)
• Full STAR 2006 geometry (with/without SVT)
  In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so (remove SVT with SVTT_OFF option)
• Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
• Using A2Emaker to get reconstructed Tower/SMD energy
  (with/without EEMC SlowSimulator in chain)
• Vertex z=0
• ~50K/per particle type
• Non-zero energy: 3 sigma above pedestal

Color coding:

• Photon (single particle MC)
• Electron (single particle MC)
• Neutral pion (single particle MC)
• Eta-meson (single particle MC)
• Eta-meson [pp2006 data] (single photons from eta-meson decay)

Pre-shower bins:

1. Ep1 = 0, Ep2 = 0 (no energy in both EEMC pre-shower layers)
2. Ep1 = 0, Ep2 > 0
3. 0 < Ep1 < 4 MeV
4. 4 < Ep1 < 10 MeV
5. Ep1 > 10 MeV
6. All pre-shower bins combined

Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer.
For a single particle MC it is a sum over
all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal.
For eta-meson from pp2006 data the sum is over 3x3 tower patch

Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Figure 1: Shower shape sorted by pre-shower conditions.
cAir-Fixed EEMC geometry (NO slow simulator, WITH SVT)
Ratio plot

Figure 2: Shower shape sorted by pre-shower conditions.
cAir-Fixed EEMC geometry (NO slow simulator, NO SVT)
Ratio plot

Figure 3: Shower shape sorted by pre-shower conditions.
cAir-Fixed EEMC geometry (WITH slow simulator, WITH SVT)
Ratio plot

Figure 4: Shower shape sorted by pre-shower conditions.
Old cAir-bug EEMC geometry (NO slow simulator, WITH SVT)
Click here to see the plot

Pre-shower migration with/without SVT

Starting with a fixed (50K events) for each type of particle.
Change in number of counts for a given pre-shower bin
with different detector configuration shows pre-shower migration

Figure 5: Pre-shower migration.
cAir-Fixed EEMC geometry (WITH SVT)

Figure 6: Pre-shower migration.
cAir-Fixed EEMC geometry (WITHOUT SVT)

Sampling fraction with/without Slow-simulator

Figure 7: Sampling fraction (0.05 E_reco / E_thrown).
cAir-Fixed EEMC geometry (WITHOUT Slow-simulator)

Figure 8: Sampling fraction (0.05 E_reco / E_thrown).
cAir-Fixed EEMC geometry (WITH Slow-simulator)
Slow simulator introduce some non-linearity in the sampling fraction

Figure 9: Sampling fraction (0.05 E_reco / E_thrown).
cAir-Fixed EEMC geometry (WITHOUT SVT, WITHOUT Slow-simulator)
Click here to see the plot

Figure 10: Sampling fraction (0.05 E_reco / E_thrown).
Old cAir-bug EEMC geometry (NO slow simulator, WITH SVT)
Click here to see the plot

2009.09.11 Test of corrected EEMC geometry: LOW_EM cuts

Test of corrected EEMC geometry: SVT and LOW_EM cuts

Monte-Carlo setup:

• One particle per event (only photons in this post)
• Full STAR 2006 geometry (with/without SVT, LOW_EM cuts)
  In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so (vary SVTT_OFF, LOW_EM)
  LOW_EM cut definition is given at the end of this page
• Throw particles flat in eta (1.2, 1.9), phi (0, 2pi), and pt (6-10 GeV)
• Using A2Emaker to get reconstructed Tower/SMD energy
  (this post: no EEMC SlowSimulator)
• Vertex z=0
• ~50K/per iteration
• Non-zero energy: 3 sigma above pedestal

Color coding:

• SVT, LOW_EM marked in legend as LowEM (single photon MC)
• STV, no-LOW_EM marked in legend as default (single photon MC)
• no-SVT, no-LOW_EM marked in legend as no-SVT (single photon MC)
• photon-jet candidates [pp2006] (used data points from this post)
• photons from eta-meson [pp2006]

Pre-shower bins:

1. Ep1 = Ep2 = 0 (no energy in both EEMC pre-shower layers)
2. Ep1 = 0, Ep2 > 0
3. 0 < Ep1 < 4 MeV
4. 4 < Ep1 < 10 MeV
5. Ep1 > 10 MeV
6. All pre-shower bins combined

Note: Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer.
For a single photon MC it is a sum over
all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal.
For eta-meson/gamma-jet candidates from pp2006 data the sum is over 3x3 tower patch

Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Figure 1: Shower shape sorted by pre-shower conditions.

Figure 2: Shower shape ratio. All shapes in Fig. 1 are divided by single photon shape
for "SVT+LOW_EM" configuration (black circles in Fig. 1)

Sampling fraction

Figure 3: Sampling fraction (0.05 * E_reco/ E_thrown)

Pre/post-shower energy and migration

Figure 4: Pre-shower1 energy (all tiles)

Figure 5: Pre-shower2 energy (all tiles)

Figure 6: Post-shower energy (all tiles)

Figure 7: Pre-shower bin photon migration

Tower energy profile

Figure 8a: Energy ratio in 2x1 to 3x3 cluster
For the first 4 pre-shower bins total yield in MC is normalized to that of the data
Blue circles indicate photon-jet candidates [pp2006] (points from this post)
Same data on a linear scale

Figure 8b: Energy ratio in 2x1 to 3x3 cluster: 7 < pt < 8 and 1.2 < eta < 1.4

 

Figure 8c: Energy ratio in 2x1 to 3x3 cluster: 7 < pt < 8 and 1.6 < eta < 1.8

Figure 9: Average energy ratio in 2x1 to 3x3 cluster vs. thrown energy

Figure 10: Average energy ratio in 2x1 to 3x3 cluster vs. thrown energy

LOW_EM cut definition

LOW_EM option for the STAR geometry (Low cuts on Electro-Magnetic processes)
is equivalent to the following set of GEANT cuts:

• CUTGAM=0.00001
• CUTELE=0.00001
• BCUTE =0.00001
• BCUTM =0.00001
• DCUTE =0.00001
• DCUTM =0.00001

All these values are for kinetic energy in GeV.

 

Cut meaning and GEANT default values:

• CUTGAM threshold for gamma transport (0.001);
• CUTELE threshold for electron and positron transport (0.001);
• BCUTE threshold for photons produced by electron bremsstrahlung (-1,);
• BCUTM threshold for photons produced by muon bremsstrahlung (-1);
• DCUTE threshold for electrons produced by electron delta-rays (-1);
• DCUTM threshold for electrons produced by muon or hadron delta-rays (-1);

Some details can be found at this link and in the GEANT manual

 

2009.10.02 Jason vs. CVS EEMC geometry: sampling fraction and shower shapes

Tests with Jason geometry file (ecalgeo.g23)

Monte-Carlo setup:

• One photon per event
• EEMC only geometry with LOW_EM option
• Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
• Using A2Emaker to get reconstructed Tower/SMD energy
  (no EEMC SlowSimulator in chain)
• Vertex z=0
• ~50K/per particle type
• Non-zero energy: 3 sigma above pedestal

Color coding:

• Photon with Jason geometry (single particle MC)
• Photon with CVS (cAir fix) geometry (single particle MC)
• Eta-meson [pp2006 data] (single photons from eta-meson decay)

Sampling fraction

Figure 1: Sampling fraction vs. thrown energy (upper plot)
and vs. azimuthal angle (lower left) and rapidity (lower right)

Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Figure 2: Shower shapes

Shower shapes sorted by pre-shower energy

Pre-shower bins:

1. Ep1 = 0, Ep2 = 0 (no energy in both EEMC pre-shower layers)
2. Ep1 = 0, Ep2 > 0
3. 0 < Ep1 < 4 MeV
4. 4 < Ep1 < 10 MeV
5. Ep1 > 10 MeV
6. All pre-shower bins combined

Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer.
For a single particle MC it is a sum over
all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal.
For eta-meson from pp2006 data the sum is over 3x3 tower patch

Figure 3: Shower shapes (left) and their ratio (right)

Figure 4: Shower shape ratios

2009.10.05 Fix to the Jason geometry file

Why volume numbers has changed in Jason geometry file?

The number of nested volumes (nv),
is the total number of parent volumes for the sensitive volume
(sensitive volume is indicated by the HITS in the tree structure below).

For the Jason and CVS files this nv number seems to be the same
(see block tree structures below).
Then why volume ids id in g2t tables has changed?

The answer I found (which seems trivial to me know)
is that in the original (CVS) file ECAL
block has been instantiated (positioned) twice.
The second appearance is the prototype (East) version of the Endcap
(Original ecalgeo.g from CVS)

        if (emcg_OnOff==1 | emcg_OnOff==3) then
             Position ECAL in CAVE z=+center
        endif
        if (emcg_OnOff==2 | emcg_OnOff==3) then
             Position ECAL in CAVE z=-center ThetaZ=180
        endif

In Jason version the second appearance has been removed
(what seems natural and it should not have any effect)
(ecalgeo.g Jason edits, g23):

        IF (emcg_OnOff>0) THEN
           Create ECAL

              .....

        IF (emcg_OnOff==2 ) THEN
           Prin1
             ('East Endcap has been removed from the geometry' )
        ENDIF               EndIF! emcg_OnOff

Unfortunately, this affects the way GEANT counts nested volumes

 

(effectively the total number was reduced by 1, from 8 to 7)

 

and this is the reason why the volume numbering scheme

 

in g2t tables has been affected.

 

I propose to put back these East Endcap line back,

 

since in this case it  will not require any additional

 

changes to the EEMC decoder and g2t tables.

 

 

Block tree of the geometry file

blue - added volumes in Jason file
red - G10 volume removed in Jason file
HITS - sensitive volumes

 ---- Jason file ----

ECAL
 EAGA
  |EMSS
  |  -EFLP
  |  |ECVO
  |  |  |EMOD
  |  |  |  |ESEC
  |  |  |  |  |ERAD
  |  |  |  |  | -ELED
  |  |  |  |  |EMGT
  |  |  |  |  |  |EPER
  |  |  |  |  |  |  |ETAR
  |  |  |  |  |  |  |  -EALP
  |  |  |  |  |  |  |  -ESCI -> HITS
  |  |ESHM
  |  |  |ESPL
  |  |  |  |EXSG
  |  |  |  |  -EXPS
  |  |  |  |  -EHMS -> HITS
  |  |  |  |  -EBLS
  |  |  |  |  -EFLS
  |  |  |ERSM
  |  -ESSP
  |  -ERCM
  |  -EPSB
  |ECGH
  |  -ECHC

---- CVS file ----
ECAL
 EAGA
  |EMSS
  |  -EFLP
  |  |ECVO
  |  |  |EMOD
  |  |  |  |ESEC
  |  |  |  |  |ERAD
  |  |  |  |  | -ELED
  |  |  |  |  |EMGT
  |  |  |  |  |  |EPER
  |  |  |  |  |  |  |ETAR
  |  |  |  |  |  |  |  -EALP
  |  |  |  |  |  |  |  -ESCI -> HITS
  |  |ESHM
  |  |  |ESPL
  |  |  |  |EXSG
  |  |  |  |  -EHMS -> HITS
  |  |  |  -EXGT
  |  |  -ERSM
  |  -ESSP
  |  -ERCM
  |  -EPSB
  |ECGH
  |  -ECHC

 

 

Block definitions

Jason geometry file 

Create ECAL

Block ECAL is one EMC EndCap wheel
  Create and Position EAGA AlphaZ=halfi
EndBlock

Block EAGA IS HALF OF WHEEL AIR VOLUME FORTHE ENDCAP MODULE
  Create AND Position EMSS konly='MANY'
  Create AND Position ECGH alphaz=90 kOnly='ONLY'
EndBlock

Block EMSS is the steel support of the endcap module
  Create AND Position EFLP z=zslice-center+zwidth/2
  Create AND Position ECVO z=zslice-center+zwidth/2
  Create AND Position ESHM z=zslice-center+zwidth/2 kOnly='MANY'
  Create AND Position ECVO z=zslice-center+zwidth/2
  Create AND Position ESSP z=zslice-center+zwidth/2
  Create ERCM
  Create EPSB
EndBlock

Block ECVO is one of endcap volume with megatiles and radiators
  Create AND Position EMOD alphaz=d3 ncopy=i_sector
EndBlock

Block ESHM is the shower maxsection
  Create and Position ESPL z=currentk Only='MANY'
  Create ERSM
EndBlock

Block ECGH is air gap between endcap half wheels
  Create ECHC
EndBlock

Block ECHC is steel endcap half cover
EndBlock

Block ESSP is stainless steelback plate 
EndBlock

Block EPSB IS A PROJECTILE STAINLESS STEEL BAR
EndBlock

Block ERCM is stainless steel tie rod in calorimeter sections
EndBlock

Block ERSM is stainless steel tie rod in shower max
EndBlock

Block EMOD (fsect,lsect) IS ONE MODULEOF THE EM ENDCAP
  Create AND Position ESEC z=section-curr+secwid/2
EndBlock

Block ESEC is a single em section
  Create AND Position ERAD z=length+(cell)/2+esec_deltaz
  Create AND Position EMGT z=length+(gap+cell)/2+esec_deltaz
  Create AND Position ERAD z=length+cell/2+esec_deltaz
EndBlock

Block EMGT is a 30 degree megatile
  Create AND Position EPER alphaz=myPhi
EndBlock

Block EPER is a 5 degree slice of a 30 degree megatile (subsector)
  Create and Position ETAR x=(rbot+rtop)/2ort=yzx
EndBlock

Block ETAR is a single calorimeter cell, containing scintillator, fiber router, etc...
  Create AND Position EALP y=(-megatile+emcs_alincell)/2
  Create AND Position ESCI y=(-megatile+g10)/2+emcs_alincell _
EndBlock

Block ESCI is the active scintillator (polystyrene) layer
EndBlock

Block ERAD is the lead radiator with stainless steel cladding
  Create AND Position ELED 
EndBlock

Block ELED is a lead absorber plate
EndBlock

Block EFLP is the aluminum (aluminium) front plate of the endcap
EndBlock

Block EALP is the thin aluminium plate in calorimeter cell
EndBlock

Block ESPL is the logical volume containing an SMD plane
  Create and Position EXSG alphaz=d3 ncopy=isec kOnly='MANY'
  Create and Position EXSG alphaz=d3 ort=x-y-z ncopy=isec kOnly='MANY'
  Create and Position EXSG alphaz=d3 ncopy=isec kOnly='MANY'
  Create and Position EXSG alphaz=d3 ort=x-y-z ncopy=isec kOnly='MANY'
  Create and Position EXSG alphaz=d3 ncopy=isec kOnly='MANY'
EndBlock

Block EXSG Is another logical volume... this one acutally creates the planes
  Create and Position EXPS kONLY='MANY'
  Create and Position EHMS x=xc y=yc alphaz=-45 kOnly='ONLY'
  Create and Position EBLS x=xc y=yc z=(+esmd_apex/2+esmd_back_layer/2) alphaz=-45 kOnly='ONLY'
  Create and Position EHMS x=xc y=yc alphaz=-45 ort=x-y-z kOnly='ONLY'
  Create and Position EFLS x=xc y=yc z=(-esmd_apex/2-esmd_front_layer/2) alphaz=-45 ort=x-y-z kOnly='ONLY'
EndBlock

Block EHMS defines the triangular SMD strips
Endblock! EHMS

Block EFLS is the layer of material on the front of the SMD planes
EndBlock! EFLS

Block EBLS is the layer of material on the back of the SMD planes
EndBlock! EFLS

Block EXPS is the plastic spacer in the shower maximum section
EndBlock

 

CVS geometry file 

Create ECAL

Block ECAL is one EMC EndCap wheel
  Create and Position EAGA AlphaZ=halfi
EndBlock

Block EAGA is half of wheel air volume forthe EndCap module
  Create and Position EMSS konly='MANY'
  Create and Position ECGH AlphaZ=90 konly='ONLY'
EndBlock

Block EMSS is steel support of the EndCap module
  Create and Position EFLP z=zslice-center+slcwid/2
  Create and Position ECVO z=zslice-center+slcwid/2
  Create and Position ESHM z=zslice-center+slcwid/2
  Create and Position ECVO z=zslice-center+slcwid/2
  Create and Position ESSP z=zslice-center+slcwid/2
  Create ERCM
  Create EPSB
EndBlock

Block ECVO is one of EndCap Volume with megatiles and radiators
  Create and Position EMOD AlphaZ=d3 Ncopy=J_section
EndBlock

Block ESHM is the SHower Maxsection
  Create and Position ESPL z=current
  Create ERSM
Endblock

Block ECGH is air Gap between endcap Half wheels
  Create ECHC
EndBlock

Block ECHC is steel EndCap Half Cover
EndBlock

Block ESSP is Stainless Steelback Plate 
endblock

Block EPSB is Projectile Stainless steel Bar
endblock

Block ERCM is stainless steel tie Rod in CaloriMeter sections
endblock

Block ERSM is stainless steel tie Rod in Shower Max
endblock

Block EMOD is one moduleof the EM EndCap
  Create and Position ESEC z=section-curr+secwid/2
endblock

Block ESEC is a single EM section
  Create and Position ERAD z=len + (cell)/2
  Create and Position EMGT z=len +(gap+cell)/2
  Create and Position ERAD z=len + cell/2
Endblock

Block EMGT is a megatile EM section
  Create and Position EPER AlphaZ=(emcs_Nslices/2-isec+0.5)*dphi
Endblock

Block EPER is a EM subsection period (super layer)
  Create and Position ETAR x=(RBot+RTop)/2ORT=YZX
EndBlock

Block ETAR is one CELL of scintillator, fiber and plastic
  Create and Position EALP y=(-mgt+emcs_AlinCell)/2
  Create and Position ESCI y=(-mgt+G10)/2+emcs_AlinCell _
EndBlock

Block ESCI is the active scintillator (polystyren) layer
endblock

Block ERAD is radiator 
  Create and PositionELED 
endblock

Block ELED is lead absorber Plate 
endblock

Block EFLP is First Aluminium plate 
endblock

Block EALP is ALuminiumPlate in calorimeter cell
endblock

Block ESPL is one of the Shower maxPLanes
  Create and position EXSG AlphaZ=d3Ncopy=isec
  Create and position EXSG AlphaZ=d3Ncopy=isec
  Create and position EXGT z=msecwd AlphaZ=d3
  Create and position EXSG AlphaZ=d3 ORT=X-Y-Z Ncopy=isec
  Create and position EXGT z=-msecwd AlphaZ=d3
  Create and position EXSG AlphaZ=d3Ncopy=isec
  Create and position EXGT z=msecwd AlphaZ=d3
  Create and position EXSG AlphaZ=d3 ORT=X-Y-Z Ncopy=isec
  Create and position EXGT z=-msecwd AlphaZ=d3
Endblock

Block EXSG is the Shower maxGap for scintillator strips
  Create EHMS
endblock

Block EHMS is sHower Max Strip
Endblock

Block EXGT is the G10 layer in the Shower Max
EndBlock