Ilya Selyuzhenkov January 30, 2008
jet trees by Murad Sarsour for pp2006 run, runId=7136022 (~60K events, no triggerId cuts yet)
R_EM1 >0.9 and R_EM2 < 0.9
cos(phi1 - phi2) < -0.9
nChargeTracks1 < 2
0 < nEEMCtowers1 < 3
Ilya Selyuzhenkov February 13, 2008
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.
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.
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:
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).
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.
In addition, fit results assuming gamma (single Gaussian, red line) or
neutral pion (double Gaussian, blue line ~ red+green) hypotheses are given.
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.
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).
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).
Trying to isolate the real gammas which hits the calorimeter,
I have sorted events into different subsets based on the following set of cuts:
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):
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.
Ilya Selyuzhenkov February 20, 2008
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
jet trees by Murad Sarsour for pp2006 run, number of runs processed: 323
4.7M di-jet events found (no triggerId cuts yet)
R_EM1 >0.9 and R_EM2 < 0.9
cos(phi1 - phi2) < -0.9
nChargeTracks1 < 2
0 < nEEMCtowers1 < 3
Ilya Selyuzhenkov February 27, 2008
Gamma-jet isolation cuts:
R_EM1 >0.9 and R_EM2 < 0.9
cos(phi1 - phi2) < -0.8
nCharge1 = 0
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.
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.
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).
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)
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
Ilya Selyuzhenkov March 03, 2008
Data set: ppLongitudinal, runId = 7136033.
Some observations/questions:
In general distributions look clean and good
Sectors 7 and 9 for v-plane and sector 7 for u-plane are noise.
Sector 9 has a hot strip (id ~ 120)
In sector 3, strips id=0-5 in v-plane are hot (see figure 2 right, bottom)
Sectors 2 and 8 in u-plane and sectors 3 and 9 in v-plane have missing strips id=283-288?
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
Ilya Selyuzhenkov March 12, 2008
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
pdf file (first 100 events) with event by event EEMC response for the candidates reconstructed into pion mass (gammaFraction >0.75)
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.
Ilya Selyuzhenkov March 20, 2008
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
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
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
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
Ilya Selyuzhenkov March 26, 2008
Definitions:
All results are for combined distributions from u and v planes: ([u]+[v])/2
Gamma-jet isolation cuts described here
Additional quality cuts:
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
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
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
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
Ilya Selyuzhenkov March 28, 2008
One interpretation of this can be that in Monte Carlo simulations
the contribution from the material in front of the detector is underestimated
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)
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)
Ilya Selyuzhenkov April 02, 2008
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
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
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
Ilya Selyuzhenkov April 03, 2008
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.
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
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.
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
Figure 2:Gamma vs jet transverse momentum.
Figure 3:Gamma vs jet azimuthal angle.
Figure 4:Gamma vs jet pseudo-rapidity.
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
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
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
Figure 20:chi2 distribution using "standard" MC shape
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)
Figure 4: Number of events which passed various cuts (MC data, partonic pt 9-11)
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
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
Ilya Selyuzhenkov April 23, 2008
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)
Ilya Selyuzhenkov May 05, 2008
Data samples:
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)
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
Ilya Selyuzhenkov May 08, 2008
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.
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.
Ilya Selyuzhenkov May 09, 2008
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).
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)
Ilya Selyuzhenkov May 14, 2008
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)
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.
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.
Ilya Selyuzhenkov May 20, 2008
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
Data sample:
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
Ilya Selyuzhenkov May 21, 2008
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)
Ilya Selyuzhenkov May 27, 2008
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 comparison between different data sets:
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.
Ilya Selyuzhenkov May 30, 2008
Three data sets:
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.
Ilya Selyuzhenkov June 04, 2008
Three data sets:
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.
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.
Ilya Selyuzhenkov June 09, 2008
Three data sets:
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.
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:
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
Ilya Selyuzhenkov June 18, 2008
Photon-jet reconstruction with the EEMC detector - Part 1: pdf or odp
Data samples (pp2006, MC gJet, MC QCD bg)
and gamma-jet reconstruction algorithm:
Comparing pp2006 with Monte-Carlo simulations scaled to the same luminosity
(EEMC pre-shower sorting):
EEMC SMD shower shapes from different data samples
(pp2006 and data-driven Monte-Carlo):
Sided residual plots: pp2006 vs data-driven Monte-Carlo
(gammas from eta meson: 3 gaussian fits)
Various cuts study:
Some QA plots:
A_LL reconstruction technique:
Work in progress... To do list:
Ilya Selyuzhenkov July 07, 2008
Data sets:
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)
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.
Ilya Selyuzhenkov July 16, 2008
Three data sets:
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
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.
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).
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]
Ilya Selyuzhenkov July 22, 2008
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).
Figure 8: Number of library candidates per sector.
Figure 9: Transverse momentum vs. energy.
Figure 10: Distance from center of the detector vs. energy.
Ilya Selyuzhenkov July 29, 2008
Data sets:
Latest data-driven shower shape replacement library:
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]
Ilya Selyuzhenkov August 14, 2008
Data sets:
Data-driven maker with bug fixed multi-shape replacement:
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]
Ilya Selyuzhenkov August 19, 2008
Data sets:
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]
Ilya Selyuzhenkov August 25, 2008
Data sets:
Event selection:
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.
Ilya Selyuzhenkov August 26, 2008
Data sets:
Data-driven library:
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]
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]
Ilya Selyuzhenkov August 27, 2008
Data sets:
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.
Ilya Selyuzhenkov September 02, 2008
Data sets:
Shower shape fitting procedure:
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 ))"
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:
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
Ilya Selyuzhenkov September 09, 2008
Data sets:
Procedure to calculate maximum sided residual:
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).
Integrate energy from a fit within +-2 strips from high strip.
This is our peak energy from fit, F_peak.
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.
Plot F_peak vs. max(D_tail-F_tail). This is sided residual plot.
(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)
Ilya Selyuzhenkov September 16, 2008
These results are obsolete.
Please use this link instead
Data sets:
Notations used in the plots:
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
Ilya Selyuzhenkov September 23, 2008
Data sets:
Notations used in the plots:
Figure 1: D_peak from [U+V]/2.
Figure 2: (D_peak - F_peak)/D_peak asymmetry
Ilya Selyuzhenkov September 23, 2008
Figure 1: D_peak vs. [right-left] D_tail
Ilya Selyuzhenkov September 23, 2008
Data sets:
Notations used in the plots:
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 7: D_peak vs. gamma 3x3 tower cluster energy
Figure 8: 3x3 cluster tower energy distribution
Figure 9: Gamma pt distribution
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]
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:
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)
Ilya Selyuzhenkov September 30, 2008
Data sets:
Notations used in the plots:
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)
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)
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):
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:
Ilya Selyuzhenkov October 14, 2008
Data sets:
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)
Ilya Selyuzhenkov October 15, 2008
Data sets:
Some observations:
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)
Ilya Selyuzhenkov October 15, 2008
Data sets:
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)
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
Ilya Selyuzhenkov October 21, 2008
Data sets:
Some comments:
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
Ilya Selyuzhenkov October 27, 2008
Data sets:
Shower shapes scaling options in data-driven maker:
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)
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:
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2: Case B
Figure 3: Case C
Figure 4: Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Ilya Selyuzhenkov November 06, 2008
Ilya Selyuzhenkov November 11, 2008
Data sets:
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
List of cuts (sorted by bin number in Figs. 2 and 3):
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
Ilya Selyuzhenkov November 18, 2008
all gamma-jet candidate selection cuts except 3x3/r=0.7 energy isolation cut
Figure 4b: 3x3/0.7 ratio but only using towers which passed jet finder threshold
Ilya Selyuzhenkov November 21, 2008
all gamma-jet candidate selection cuts except 3x3/r=0.7 energy isolation cut
There are two sets of figures in links below:
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):
Gamma candidate detector eta > 1.5:
(smaller tower size)
Figure 1a: 2x1/3x3 energy fraction [number of counts per given fraction]
Figure 2a: 2x2/3x3 energy fraction [number of counts per given fraction]
Ilya Selyuzhenkov November 25, 2008
Fig.2 [lower right, 5th bin] shows that
charge particle veto also independent from other cuts
List of cuts (sorted according to bin number in Figs. 1-3. [No SMD sided residual cuts]):
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
Ilya Selyuzhenkov December 08, 2008
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
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.
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):
Ilya Selyuzhenkov December 09, 2008
Ilya Selyuzhenkov December 09, 2008
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):
Ilya Selyuzhenkov December 09, 2008
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):
Ilya Selyuzhenkov December 09, 2008
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):
Ilya Selyuzhenkov December 09, 2008
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):
Ilya Selyuzhenkov December 09, 2008
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):
Ilya Selyuzhenkov December 11, 2008
Presentation in pdf or open office file format
Ilya Selyuzhenkov December 16, 2008
Gamma-jet isolation cuts except 3x3/r=0.7 energy isolation cut
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.
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
Ilya Selyuzhenkov December 19, 2008
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