Paper Proposal

Cross section and longitudinal double-spin asymmetry for inclusive charged pion production in polarized proton collisions at sqrt(s) = 200 GeV

Proposed Target Journal

Phys. Rev. Lett.

Principal Authors

A. Kocoloski, L. Ruan, B. Surrow, Y. Xu, Z. Xu

Abstract

We report an extension in transverse momentum to 15 GeV/c for the differential cross section and a first measurement of the longitudinal double-spin asymmetry A_{LL} for inclusive midrapidity charged pion production in polarized proton collisions at sqrt(s) = 200 GeV. The pi-/pi+ ratio shows significant p_{T} dependence from unity at low p_{T} to 0.83±0.01(stat)±0.04(syst) at high p_{T}, an experimental signature of significant valence quark contribution. The next-to-leading order perturbative QCD models incorporating flavor dependence in the fragmentation functions can describe the cross sections and the isospin asymmetry. The charged pion A_{LL} data covering 2 < pT < 13 GeV/c disfavor a large polarized gluon distribution in the proton and provide the first glimpse of a direction of future measurements. The predicted A_{LL} difference in the positively and negatively charged pions can be tested in future RHIC data.

Summary

In summary, we report the differential cross section and longitudinal double-spin asymmetry A_{LL} for inclusive charged pion production at midrapidity in polarized proton collisions at sqrt(s) = 200 GeV. The pion cross sections were determined up to p_{T} = 15 GeV/c and are described by NLO pQCD evaluations over 7 orders of magnitude. The asymmetries A_{LL} cover 2 < p_{T} < 13 GeV/c. They are consistent with NLO pQCD calculations utilizing polarized quark and gluon distributions from inclusive DIS analyses and disfavor large positive values of gluon polarization in the polarized nucleon.

Proposed Figures

Figure 1: “Technical” or “instrumental” figure on the use of a jet patch trigger for this analysis. Final graphics are still to be determined. The left panel shows the minimum-bias and triggered distributions obtained from PYTHIA simulations and used to correct the measured pion spectra. The right panel plots the mean fraction of the jet momentum carried by associated (R=0.4) charged pions in the case of jets that fired the trigger (black filled squares) and jets opposite trigger jets (red open squares). The inset shows the ($\eta$, $\phi$) distribution of charged pions relative to the trigger jet in the data.

Figure 2: Transverse momentum spectra for charged pions at midrapidity ($\left|{y}\right| < 0.5$) (a) and comparison of data to DSS prediction versus $p_{T}$ (b). (c) shows ratio of π-/π+ data compared to DSS and AKK predictions.

Figure 3: The longitudinal double-spin asymmetry $A_{LL}$ in $\vec{p} + \vec{p} \rightarrow \pi^{+/-} + X$ at $\sqrt{s}=200 GeV$ versus pion $p_{T}$. The uncertainties on the data points are statistical. The gray bars indicate the total point-to-point systematic uncertainty, and the curves show predictions based on parameterizations of gluon polarization from global analyses.

Supporting Documentation

Asymmetry :: Cross Section

 

Mike M., Feb 02, 2007

<pre>
Hi all,
some brief comments after reading the newest draft (v2).

1) Overall, I really like the way the first page reads -- well done.

2) I think it's lacking a crisp explanation of why online jets are  used instead of offline, perhaps I missed it.

3) I think that Figure 2 should clearly be labeled as "Simulation".   It's in the caption, but the minute the figure gets clipped into  a  presentation the caption (and that info) are gone.

4) I would prefer the use of "Fast Monte Carlo" instead of "toy  model," for various reasons.  First, anyone in particle physics will  probably understand that Fast MC implies not doing full simulation/ reconstruction, but a quick smearing.  Furthermore, there's enough  info in the MC that you believe the results, whereas the phrase "toy"  makes it sound otherwise. Second, anybody outside of particle physics  won't know the difference between a fast MC and a full slow simulation.

5) Personally I find Figure 3 fairly confusing, especially when  dumped to BW.  I realize this comes down to matters of taste, but I  personally prefer to show the model calculations as binned histograms  (TH1::Draw("hist")).  The markers are in my mind too easily confused  with the data (given that this is really 6 plots).  Further, the  "hist" option will clearly show that the models were binned the same  as the data, and makes a nice distinction between data/theory.  By a  clever choice of line styles, you can probably even make this fairly  clear in BW.

6) In Figure 3 and in the text, would it be possible to use some  english to differentiate between A_N(>pi) vs A_N(<pi).  Maybe  introduce the phrases "quark-like" and "gluon-like"?  If you can find  a smooth way, it would sure make the p4 text smoother, and give some  intuition to fig 3.
I realize it'sa little tricky because Fig3b is kind of an orphan.  Do  you really need it?  They're straight lines , and you could quote 4  numbers in the text in a single sentence, even cutting down on some  space.  Then you could have a "quark-like" left panel and a "gluon- like" right panel.

But, again, I really like the draft.

-Mike

Peter J., Jan 4, 2007

Hi GPC and PAs,

Here are some first comments on the paper draft:

(i) physics intro, 1st para: I find the physics intro to be a bit confusing. I looked briefly at refs [6,7] (Brodsky et al and Collins). Within my distinctly limited understanding of them I don't think the last sentence of the first paragraph is accurate, and the main physics point of interest in this measurement is missed.

Evidently, the SSA arises due to interference of left- and right-handed quark polarization states, and thus is sensitive to chiral symmertry breaking. If correct, this is important and should be featured prominently in the intro.

On the other hand, I am confused by the various claims about factorization in this process. Brodsky et al claim that the process cannot be factored into PDF and FF, while Collins claims that factorization holds but then derives a pdf $f_{1T}^\perp$ whose sign is opposite for DIS and DY (eq 3), i.e. a pdf whose value is process-dependent, which doesn't sound to me like factorization. The only thing I know for sure is that I am confused on this point and could use some guidance. I suspect that most non-experts will be similarly confused. The physics intro should be precise and clear about what the theory says.

(ii) p 1 left col 2nd para bottom: what is the specific relevance to this measurement of the inclusive jet cross section being described by (factorized) pQCD? I guess if it didn't work for the inclusive yields one could stop immediately. Is that the only point to be made here? Can one say more about constraints on PDFs and FFs?

(iii) p 1 right col line 9: I know nothing about Siberian snakes. What are the limits on possible non-vertical polarization states?

(iv) End of that para: give errors on polarization: 59\pm{xx}\% (57\pm{yy}\%).

(v) jet reconstruction: nowhere do you actually describe what "jet reconstruction" you do. P 2 left col line 6 talks about "jet clusters at level 2" and the caption of Fig 1 talks about "full jet reconstrcution" but the reader is left hanging about how a jet is actually defined. Is there some peak-finding with a cut-off radius, or what? I know that you use EMC energy only but the non-expert reader will not know what this implies, i.e. all of the EMC energy plus perhaps 30% of the charged hadronic energy, with some charged-track dispersion in the magnetic field that is not corrected for. You need a couple of paragraphs defining the jet finding used for the analysis and giving its comparison to full jet reco, justifying why this technique is adequate for this measurement (there is currently some of that later in the text but it should be consolidated).

(vi) Fig 1d: why only 2% of the data? I think I know the answer: that's what was reconstructed at the time you were in the thick of this analysis, but evidently more has been done in the meantime. Not usable?

(vii) p 2 left col middle: I printed the paper in B&W and don't see the 6-fold L0 peaks in fig 1a. Am I missing them?

(viii) p 2 right col 2nd para: the "favoring" of qg vs gg at forward vs midrapidity is qualitative. Can this be made quantitative, e.g using PYTHIA? What is the magnitude of the variation of the two contributions?

(ix) p 2 right col middle: "while we away the time-consuming replay of the full dataset including TPC..." is a STAR detail of little interest to others, and has a limited shelf-life. I suggest simply describing what was done, saying that this rapid analysis technique (in contrast to full jet reco) is sufficient for present purposes.

(x) Fig 1b and discussion of tails in p2 right col bottom: "might reflect moderately hard gluon emission" is weak. Can this be studied with a model calculation? But I also find it confusing because I don't know how the jet finding was done. Hard gluon emission will generate an acoplanarity only if it pushes some of the energy flow out of the jet cone, otherwise momentum is conserved. So I suspect that this tail depends on how the jet is defined. Needs more discussion.

I also wonder about tails being generated by the combination of relatively low multiplicity in low energy jets and only partial jet reco (EM plus ~30% hadronic, with some funny spread in the latter due to the field). Could unfavorable, perhaps rare, fluctuations in charged vs neutral pions generate such apparent tails which are not present for full jet reco? Perhaps a model study would help here. Anyway, the toy model in which you just fit with a Gaussian with an exponential seems inadequate - can you do a more meaningful study based on PYTHIA or HERWIG?

(xi) p 3 left col top: is there a jet energy dependence to <kT^2>? More generally, the distribution shown in Fig 1d goes out to ~50 GeV if I jack it up by eye by a factor 50. Can you make a few coarse energy bins to look at the dependence of the asymmetry on jet energy? You say somewhere that you expect the ET dependence of the Sivers effect to be small, but surely it would be good to test this.

(xii) definitions of A_N and r_\pm (eq 1 and 2): it's late in the evening and I am a bit tired, but frankly these formulas are not speaking to me at the moment. A_N is defined as the ratio of ratios, which is OK, but I am not getting the purpose of the sqrt. There are too many +- and -+ subscripts and zeta>pi vs zeta<pi which are hard to distinguish. Can you find a more transparent notation, or explain the structure of the definitions a bit better?

That's all for now. I didn't read the last third as carefully, I'll do that next time.

Hope these are helpful, talk to you tomorrow.

Peter

BEMC Calibration

This page is intended to provide documentation of how I arrived at the numbers and figures relevant to the BEMC calibration quoted in the paper.  Data and macros can be found at

http://deltag5.lns.mit.edu/~kocolosk/protected/long_jet_paper/

If you want to reproduce these plots, you're probably best off just downloading the whole directory.  Here's a tarball:

http://deltag5.lns.mit.edu/~kocolosk/protected/long_jet_paper/bemcCalibration.tar

2003

Nothing to see here just yet.  I asked Alex about the availability of the data and was told that they used the last dAu combined production.

2004

Run 4 BTOW Calibration

Nevents = 20-57 M.  Run 4 is funny because we've reproduced P05ia since I did my analysis, so the old query I used isn't really valid anymore.  I took these numbers from Jamie's page at

http://www.star.bnl.gov/protected/common/common2004/trigger2004/200gev/sums_emc.txt

The 20M number is the total number of trigId==15007 events.  These are what we used for the MIP calibration, but we also applied centrality and vertex cuts that aren't reflected in that number.  The number is also complicated by the warning not to use minbias triggers from productionLow || Mid || High before 5042040, which I did not heed (not sure if we knew about the problem at the time).  For the electrons we required trigId!=15203, which if I take all-bTid15203 from that link gives me 57.2M events.

No attempt has been made to figure out exactly what fraction of these data was actually analyzed in my jobs.  In Run 5 that number was 94%, here I've just taken it to be 100%.

Nmips = 1.4 x 10^{6}.  This is the number of entries in the "mH0" histogram in 2004_mips.root

Nelectrons = 1.5 x 10^{6}  makeElectronCounts.C The reason we have Nmips ~= Nelectrons is that the electron sample dropped the refMult and vertex cuts and took some triggered data as well.  Specifically, we used trigId==15007 for the MIPs and trigId!=15203 for the electrons.

Figure 2a:  makeMipFigures.C

Figure 4a:  makeElectronFigure2.C

Figure 5:  makeGainsFigure.C

Note that in each case you'll have to download the ROOT files required by the macro and put them in the same directory.

2005

Run 5 BTOW Calibration

Nevents = 57M.  This one is a bit tricky, as I never saved a tree with all the events I looked at.  Here's how I arrived at the number.  I used a catalog query


to get number of files and events for each trgsetupname I used in the calibration (ppProduction || ppProductionMinBias || ppTransProduction).  The query returned a total of 73716 files in HPSS.  I did save my original filelists, and when I cat them together I find that I analyzed about 94% of available files (69297 to be exact).  The event sample from catalog was 60.8 M events, and I just multiplied that number by the percentage of analyzed files.  This doesn't reflect the |vz|<30 cut that was applied to the MIP study.

Nmips = 4.3 x 10^{6}  This is the number of entries in "h" in 2005_mips.root.  All the cuts from Run 5 BTOW Calibration had already been applied.  You might have noticed that this number is much larger than the number of MIPs in Run 4.  The reason for this is that in Run 4 we used only minbias (trigId == 15007) and we also applied a centrality cut of 60-100% (refMult<57).  In addition, it's quite a lot harder to find an isolated track in a AuAu event versus a pp event, and Run 5 had a 3/4 barrel instead of just 1/2.

Nelectrons = 0.4 x 10^{6}  makeElectronCounts.C applies all the cuts and spits out the final counts for 2004 and 2005.

Figure 2b:  makeMipFigures.C

Figure 4b:  makeElectronFigure2.C

Figure 5:  makeGainsFigure.C

Figure 8:  makeElectronFigure3.C

a) Statistics of Lambda and anti-Lambda Reconstruction, Inclusive

D_TT analysis Record, Rec_Step: all_cut_crp0995

Cuts Summary

Here's a list of the cuts applied to the data in the common spin trees.

Run 5

Event

• standard spinDB requirements
• production triggers only
  

ConeJets

• 0.2 < detEta < 0.8
• 0.1 < E_neu / E_tot < 0.9

chargedPions

• pt > 2
  
• -1 < eta < 1
• nFitPoints > 25
• |DCA_global| < 1
• -1 < nSigmaPion < 2

bemcPions

• pt > 3.0
  
• photon energies > 0.1
• asymmetry < 0.8
• 0.08 < mass < 0.25
• charged track veto
• BBC timebin in {7,8,9}

Run 6

Event

• standard spinDB requirements
• production triggers + trigId 5 (L2gamma early runs)

ConeJets, ConeJetEMC -- no cuts applied

chargedPions

• pt > 2
  
• -1 < eta < 1
• nFitPoints > 25
• |DCA_global| < 1
• -1 < nSigmaPion < 2

bemcPions

• pt > 5.2
• photon energies > 0.1
• asymmetry < 0.8
• 0.08 < mass < 0.25
• charged track veto
• BBC timebin in {7,8,9} update:  timebin 6 added in 2007-07-18 production
• both SMD planes good
  

bemcElectrons added as of 2007-07-18 production

• hardware or software trigger in (117001, 137213, 137221, 5, 137222, 137585, 137611, 137622)
• Global dE/dx cut changing with momentum
• nFitPoints >= 15
• nDedxPoints >= 10
• nHits / nPoss >= 0.52
• track Chi2 < 4
• DCAGlobal < 2
• NEtaStrips > 1 && NPhiStrips > 1
• Primary dE/dx cut changing with momentum
• 0.3 < P/E < 1.5
• -0.01287 < PhiDist < 0.01345
• ZDist in [-5.47,1.796] (West) or [-2.706,5.322] (East)

Introduction at Spin PWG meeting - 5/10/07

I've been working on a project to make the datasets from the various longitudinal spin analyses underway at STAR available in a common set of trees.  These trees would improve our ability to do the kind of correlation studies that are becoming increasingly important as we move beyond inclusive analyses in the coming years.

In our current workflow, each identified particle analysis has one or more experts responsible for deciding just which reconstruction parameters and cuts are used to determine a good final dataset.  I don't envision changing that.  Rather, I am taking the trees produced by those analyzers as inputs, picking off the essential information, and feeding it into a single common tree for each run.  I am also providing a reader class in StSpinPool that takes care of connecting the various branches and does event selection given a run list and/or trigger list.

Features

• Readable without the STAR framework
  
• Condenses data from several analyses down to the most essential ~10 GB (Run 6)
• Takes advantage of new capabilities in ROOT allowing fast fill/run/trigger selection

Included Analyses

• Event information using StJetSkimEvent
• ConeJets12 jets (StJet only)
• ConeJetsEMC jets (StJet only)
• charged pions (StChargedPionTrack)
  
• BEMC neutral pions (TPi0Candidate)
  
• EEMC neutral pions (StEEmcPair?) -- TODO
• electrons * -- TODO
  
• ...
  

Current Status

I'm waiting on the skimEvent reproduction to finish before releasing.  I've got the codes to combine jets, charged pions, and BEMC pions, and I'm working with Jason and Priscilla on EEMC pions and BEMC electrons.

2006.07.31 First Look at SMD gamma/pi0 Discrimination

 

Pibero Djawotho

 

Indiana University
July 31, 2006

Simulation

Simulation were done by Jason for the SVT review.

Maximal side residual

Figure 1: Fitted peak integral vs. fit residual sum (U+V) from st_jpsi input stream (J/psi trigger only).	Figure 2: Fitted peak integral vs. fit residual sum (U+V) from st_physics input stream (all triggers except express stream triggers).

xy distribution of SMD hits

The separation between photons and pions was achieved by using Les cut in the above figures where photons reside above the curve and pions below. The data set used is the st_jpsi express stream.

Single peak characteristics

Fit function

The transverse profile of an electromagnetic shower in the SMD can be parametrized by the equation below in each SMD plane:

f(x) is the energy in MeV as a function of SMD strip x. The algorithm performs a simultaneous fit in both the U and V plane. The maximal residual (data - fit) is then calculated. A single photon in the SMD should be well descibed by the equation above and therefore will have a smaller maximal residual. A neutral pion, which decays into two photons, should exhibit a larger maximal residual. Typically, the response would be a double peak, possibly a larger peak and a smaller peak corresponding to a softer photon.

Single event SMD response

This directory contains images of single event SMD responses in both U and V plane. The file name convention is SMD_RUN_EVENT.png. The fit function for a single peak is the one described in the section above with 5 parameters:

• p0 = yield (P₀), area under the peak in MeV
• p1 = mean (μ), center of peak in strips
• p2 = sigma of the first Gaussian (w₁)
• p3 = fraction of the amplitude of the second Gaussian with respect to the first one (B), fixed to 0.2
• p4 = ratio of the width of the second Gaussian to the width of the first one (w₂/w₁), fixed to 3.5

Code

macros

Documents

1. Proposal to Contstruct an Endcap Calorimeter for Spin Physics at STAR
2. Appendix Simulation Studies of Direct Photon Production at STAR
3. An Endcap Calorimeter for STAR Conceptual Design Report
4. The STAR Endcap Electromagnetic Calorimeter (EEMC NIM)
5. An Endcap Calorimeter for STAR Technical Design Update #1
6. Jan's gamma/pi⁰ algorithm
7. Endcap Calorimeter Proposal (HTML @ IUCF)
8. STAR Note 401: An Endcap Electromagnetic Calorimeter for STAR--Conceptual Design Report
9. Spin Effects at Suppercollider Energies

2006.08.04 Second Look at SMD gamma/pi0 Discrimination

 

Second Look at SMD gamma/pi0 Discrimination

Pibero Djawotho
Indiana University
August 4, 2006

Dataset

The dataset used in this analysis is the 2005 p+p collision at √s=200 GeV with the endcap calorimeter high-tower-1 (eemc-ht1-mb = 96251) and high-tower-2 (eemc-ht2-mb = 96261) triggers.

The file catalog query used to locate the relevant files is:
get_file_list.pl -keys 'path,filename' -delim / -cond 'production=P05if, trgsetupname=ppProduction,filetype=daq_reco_MuDst,filename~st_physics, tpc=1,eemc=1,sanity=1' -delim 0

Results

SMD U and V Fits

Code

macros

2006.08.06 Comparison between EEMC fast and slow simulator

 

Comparison between EEMC fast and slow simulator

Pibero Djawotho
Indiana University
August 6, 2006

A detailed description of the EEMC slow simulator is presented at the STAR EEMC Web site.

The following settings were used in running the slow simulator:

  //--
  //-- Initialize slow simulator
  //--
  StEEmcSlowMaker *slowSim = new StEEmcSlowMaker("slowSim");
  slowSim->setDropBad(1);   // 0=no action, 1=drop chn marked bad in db
  slowSim->setAddPed(1);    // 0=no action, 1=ped offset from db
  slowSim->setSmearPed(1);  // 0=no action, 1=gaussian ped, width from db
  slowSim->setOverwrite(1); // 0=no action, 1=overwrite muDst values
  slowSim->setSource("StEvent");

  slowSim->setSinglePeResolution(0.1);
  slowSim->setNpePerMipSmd(2.0);
  slowSim->setNpePerMipPre(3.9);
  slowSim->setMipElossSmd(1.00/1000);
  slowSim->setMipElossPre(1.33/1000);

EEMC Fast Simulator	EEMC Slow Simulator

2006.09.15 Fit Parameters

 

Fit Parameters

Fit Function

The plots that follow are sums of individual SMD responses in each plane centered around a common mean (here 0), over a +/-40 strips range. The convention for the parameters in the fits below is:

• p0=E -- area under the curve which represents energy in MeV
• p1=μ -- mean
• p2=σ_core -- width of the narrow Gaussian
• p3=γ -- relative contribution of the wide Gaussian to the area/height
• p4=σ_tail -- width of the wide Gaussian

Simulation

The simulation is from single photons thrown at the EEMC with the following p_T distribution:

The highest tower above 4 GeV in total energy is selected and the corresponding SMD sector fitted for peaks in both planes, where the area of the peaks in the U plane is constrained to be identical to that of the peak in the V plane. The peak is shifted to be centered at 0 where peaks from other events are then summed. The summed SMD response in each plane is displayed below:

Ditto in log scale.

Ditto by sector.

Fit Widths

Sector #	SMD-u σcore	SMD-u σtail	SMD-v σcore	SMD-v σtail
Sector 1	0.869033 ± 0.0142868	3.42031 ± 0.10226	0.84379 ± 0.0185107	3.03287 ± 0.0775009
Sector 2	0.814959 ± 0.0169271	2.99941 ± 0.0730426	0.889892 ± 0.0163065	3.35288 ± 0.0911979
Sector 3	0.84862 ± 0.0148706	3.07648 ± 0.0909689	0.914377 ± 0.014706	3.72821 ± 0.0966915
Sector 4	0.924398 ± 0.0144207	3.74458 ± 0.10611	0.888146 ± 0.0180771	3.06618 ± 0.0647075
Sector 5	0.934218 ± 0.0163887	3.45149 ± 0.0944309	0.911209 ± 0.0175273	3.28633 ± 0.0890581
Sector 6	0.797976 ± 0.0148133	3.20464 ± 0.0986085	0.822437 ± 0.018835	3.30595 ± 0.118813
Sector 7	0.836936 ± 0.0150085	3.28589 ± 0.0853598	0.873338 ± 0.0173883	3.16654 ± 0.0838938
Sector 8	0.828403 ± 0.0167005	3.05517 ± 0.075584	0.891045 ± 0.0152102	3.34806 ± 0.0836394
Sector 9	0.832881 ± 0.0127855	3.3214 ± 0.0762928	0.8436 ± 0.0175466	3.0183 ± 0.079444
Sector 10	0.804059 ± 0.0160906	3.0943 ± 0.0897946	0.874845 ± 0.015788	3.18113 ± 0.0748357
Sector 11	0.930286 ± 0.0187086	3.40024 ± 0.0951671	0.854395 ± 0.0167265	3.21076 ± 0.0812402
Sector 12	3.33227 ± 0.111911	0.848668 ± 0.0142344	0.895174 ± 0.0160939	3.48527 ± 0.12061

Data from 2005 pp200 EEMC HT 1 and 2 triggers

In this sample, high tower triggers, eemc-ht1-mb (96251) and eemc-ht2-mb (96261), from the 2005 p+p at √s=200 GeV ppProduction are selected. The highest tower above 4 GeV is chosen and the corresponding SMD sector is searched for peaks in both planes. Peaks from several events are summed together taking care of shifting them around to have a common mean.

Ditto in log scale.

Data from 2005 pp200 electrons

Here, I try to pick a representative sample of electrons from the 2005 pp200 dataset. The cuts used to pick out electrons are:

• E_preshower1 > 5 MeV
• E_preshower2 > 5 MeV
• 0.75 < p/E_tower < 1.25
• 3 < dE/dx < 4 keV/cm

The selection for electrons is illustrated in the dE/dx plot below, where the pions should be on the left and the electrons on the right.

---

Pibero Djawotho

Last modified Tue Aug 15 10:41:19 EDT 2006

2007.02.05 Reconstructed/Monte Carlo Photon Energy

 

Reconstructed/Monte Carlo Photon Energy

This study is motivated by Weihong's photon energy loss study where an eta-dependence of reconstructed photon energy to generated photon energy in EEMC simulation was observed.

In this study, the eta-dependence is investigated by running the EEMC slow simulator with the new readjusted weights for the preshower and postshower layers of the EEMC. Details on this are here.

• Fit to a constant



• Fit to a line



• Fit to a quadratic



• Comparison between Weihong's and Pibero's results

The parameters from the fits are used to plot the fit functions for comparison between Weihong's and Pibero's results.

• Constant



• Linear



• Quadratic



• Conclusion

While the adjusted weights for the different EEMC layers contribute to bringing the ratio of reconstructed energy to generated energy closer to unity, they do not remove the eta-dependence.

• References

1. M. Albrow et al., NIM A 480 (2002) 524-546.
2. R. Blair et al. (CDF Collaboration), CDF II Technical Design Report, FERMILAB-PUB-96-390-E, 1996.

---

Pibero Djawotho
Last updated Mon Feb 5 10:10:42 EST 2007

2007.02.08 E_reco / E_mc vs. eta

 

E_reco / E_mc vs. eta

Legend

• black curve: before EEMC slow simulator
• red curve: after EEMC slow simulator

Jason's Monte Carlo

• 4.4k single gamma's
• No SVT
• Nominal vertex
• Flat in pt 4-12 GeV

Will's Monte Carlo

• 10k single gamma's
• SVT/SSD out
• Vertex at 0
• Flat in pt 5-60 GeV

In the plot below, I use the energy of the single tower (tower with max energy) presumably the tower the photon hit. The nonlinearity seems to disappear.

In the plot below, I use the energy of the 3x3 cluster of tower centered around the tower with the max energy. The nonlinearity is restored.

The plot below shows E_tower/E_cluster vs. eta where the cluster consists of 3x3 towers centered around the max energy tower.

Below is the profile of E_tower/E_cluster vs. eta.

The plot below shows the energy sampled by the entire calorimeter as a function of eta, i.e. sampling fraction as a function of eta.

Sampling fraction integrated over all eta's.

---

Pibero Djawotho

Last updated Thu Feb 8 13:59:29 EST 2007

2007.02.11 Reconstructed/Monte Carlo Muon Energy

 

Reconstructed/Monte Carlo Muon Energy

10k muons thrown by Will with:

• z_vertex=0
• Flat in p_T 5-60 GeV/c
• Flat in η 1.1-2

z_vertex

p_T vs. η

E_MC

E_tower/E_MC vs. η

E_cluster/E_MC vs. η

E_towertanh(eta) vs. eta

---

Pibero Djawotho

Last modified Sun Feb 11 19:51:55 EST 2007

2007.02.15 160 GeV photons

 

160 GeV photons

 

• 10k 160 GeV photons
• z_vertex=0
• η range 0.8-2.2
• SVT/SSD out

---

Pibero Djawotho

Last updated Thu Feb 15 04:33:09 EST 2007

2007.02.15 20 GeV photons

 

20 GeV photons

 

• 10k 20 GeV photons
• z_vertex=0
• η range 0.8-2.2
• SVT/SSD out

---

Pibero Djawotho

Last updated Thu Feb 15 04:32:25 EST 2007

2007.02.15 80 GeV photons

 

80 GeV photons

 

• 10k 80 GeV photons
• z_vertex=0
• η range 0.8-2.2
• SVT/SSD out

---

Pibero Djawotho

Last updated Thu Feb 15 03:16:54 EST 2007

2007.02.15 Reconstructed/Monte Carlo Electron Energy

 

Reconstructed/Monte Carlo Electron Energy

E=1 GeV	E=2 GeV

---

Pibero Djawotho

Last modified Thu Feb 15 00:42:30 EST 2007

2007.02.19 10 GeV photons

 

10 GeV photons

 

• 10k 10 GeV photons
• z_vertex=0
• η range 0.8-2.2
• SVT/SSD out

---

Pibero Djawotho

Last updated Mon Feb 19 20:35:13 EST 2007

2007.02.19 40 GeV photons

 

40 GeV photons

 

• 10k 40 GeV photons
• z_vertex=0
• η range 0.8-2.2
• SVT/SSD out

---

Pibero Djawotho

Last updated Mon Feb 19 20:37:37 EST 2007

2007.02.19 5 GeV photons

 

5 GeV photons

 

• 10k 5 GeV photons
• z_vertex=0
• η range 0.8-2.2
• SVT/SSD out

---

Pibero Djawotho

Last updated Mon Feb 19 20:13:39 EST 2007

2007.02.19 Summary of Reconstructed/Monte Carlo Photon Energy

 

Summary of Reconstructed/Monte Carlo Photon Energy

 

Description

The study presented here uses Monte Carlo data sets generated by Will Jacobs at different photon energies (5, 10, 20, 40, 80, 160 GeV):

• 10k photons
• vertex at 0
• eta 0.8-2.2
• SVT/SSD out

For each photon energy, the ratio E_reco/E_MC vs. eta was plotted and fitted to the function p0+p1*(1-eta), where E_reco is the reconstructed photon energy integrated over the

entire

EEMC. The range of the fit was fixed from 1.15 to 1.95 to avoid EEMC edge effects. The advantage of parametrizing the eta-dependence of the ratio in this way is that p0 is immediately interpretable as the ratio in the middle of the EEMC. The parameters p0 and p1 vs. photon energy were subsequently plotted for the EEMC fast and slow simulator.

EEMC Fast/Slow Simulator Results

Conclusions

The parameter p0, i.e. the ratio E_reco/E_MC in the mid-region of the EEMC, increases monotonically from 0.74 at 5 GeV to 0.82 at 160 GeV, and the parameter p1, i.e. the slope of the eta-dependence, also increases monotonically from -0.035 at 5 GeV to 0.038 at 160 GeV. There appears to be a magic energy around 10 GeV where the response of the EEMC is nearly flat across its entire pseudorapidity range. The anomalous slope p1 at 160 GeV for the EEMC slow simulator is an EEMC hardware saturation effect. The EEMC uses 12-bit ADC's for reading out tower transverse energies and is set for a 60 GeV range. Any particle which deposits more than 60 GeV in E_T will be registered as depositing only 60 GeV as the ADC will return the maximum value of 4095. This translates into a limit on the eta range of the EEMC for a particular energy. Let's say that energy is 160 GeV and the EEMC tops at 60 GeV in E_T, then the minimum eta is acosh(160/60)=1.6. This limitation is noticeable in a plot of E_reco/E_MC vs. eta. This anomaly is not observed in the result of the EEMC fast simulator because the saturation behavior was not implemented at the time of the simulation (it has since been corrected). The energy-dependence of the parameter p0 is fitted to p0(E)=a+b*log(E) and the parameter p1 to p1(E)=a+b/log(E). The results are summarized below:

EEMC fast simulator fit p0(E)=a+b*log(E)
a = 0.709946 +/- 0.00157992
b = 0.022222 +/- 0.000501239

EEMC slow simulator fit p0(E)=a+b*log(E)
a = 0.733895 +/- 0.00359237
b = 0.0177537 +/- 0.0011397

EEMC fast simulator fit p1(E)=a+b/log(E)
a = 0.0849103 +/- 0.00556979
b = -0.175776 +/- 0.0138241

EEMC slow simulator fit p1(E)=a+b/log(E)
a = 0.0841488 +/- 0.0052557
b = -0.187769 +/- 0.0130445

---

Pibero Djawotho

Last updated Mon Feb 19 23:18:44 EST 2007

2007.05.24 gamma/pi0 separation in EEMC using linear cut

 

gamma/pi0 separation in EEMC using linear cut

---

Pibero Djawotho

Last updated Thu May 24 04:41:13 EDT 2007

2007.05.24 gamma/pi0 separation in EEMC using quadratic cut

 

gamma/pi0 separation in EEMC using quadratic cut

---

Pibero Djawotho

Last updated Thu May 24 04:41:13 EDT 2007

2007.05.24 gamma/pi0 separation in EEMC using quadratic cut

 

gamma/pi0 separation in EEMC using quadratic cut

---

Pibero Djawotho

Last updated Thu May 24 04:41:13 EDT 2007

2007.05.30 Efficiency of reconstructing photons in EEMC

 

Efficiency of reconstructing photons in EEMC

Monte Carlo sample

• 10k photons
• STAR y2006 geometry
• z-vertex=0
• Flat in pt 10-30 GeV
• Flat in eta 1.0-2.1

SMD gamma/pi0 discrimination algorithm

The following

slide

from the IUCF STAR Web site gives a brief overview of the SMD gamma/pi0 discrimination algorithm using the method of maximal sided fit residual (data - fit). This technique comes to STAR EEMC from the Tevatron via Les Bland via Jason Webb. The specific fit function used in this analysis is:

f(x)=[0]*(0.69*exp(-0.5*((x-[1])/0.87)**2)/(sqrt(2*pi)*0.87)+0.31*exp(-0.5*((x-[1])/3.3)**2)/(sqrt(2*pi)*3.3))

x is the strip id in the SMD-u or SMD-v plane. The widths of the narrow and wide Gaussians are determined from empirical fits of shower shape response in the EEMC from simulation.

Optimizing cuts for gamma/pi0 separation

In the rest of this analysis, only those photons which have reconstructed pt > 5 GeV are kept. There is no requirement that the photon doesn't convert. The dividing curve between photons and pions is:

f(x)=4*x+1e-7*x**5

The y-axis is integrated yield over the SMD-u and SMD-v plane, and the x-axis is the sum of the maximal sided residual of the SMD-u and SMD-v plane.

Following exchanges with Scott Wissink, the idea is to move from a quintic to a quadratic to reduce the number of parameters. In addition, the perpendicular distance between the curve and a point in the plane is used to estimate the likelihood of a particle being a photon or pion. Distances above the curve are positive and those below are negative. The more positive the distance, the more likely the particle is a photon. The more negative the distance, the more likely the particle is a pion.

Hi Pibero,

With your new "linear plus quintic" curve (!) ... how did you choose the
coefficients for each term?  Or even the form of the curve?  I'm not
being picky, but how to optimize such curves will be an important issue
as we (hopefully soon) move on to quantitative comparisons of efficiency
vs purity.

As a teaser, please see attached - small loss of efficiency, larger gain
in purity.

Scott

Hi Pibero,

I just worked out the distance of closest approach to a curve of the form

    y(x) = a + bx^2

and it involves solving a cubic equation - so maybe not so trivial after
all.  But if you want to pursue this (not sure it is your highest
priority right now!), the cubic could be solved numerically and "alpha"
could be easily calculated.

More fun and games.

Scott

Hi Pibero,

I played around with the equations a bit more, and I worked out an
analytic solution.  But a numerical solution may still be better, since
it allows more flexibility in the algebraic form of the 'boundary' line
between photons and pions.

Here's the basic idea:  suppose the curved line that cuts between
photons and pions can be expressed as y = f(x).  If we are now given a
point (x0,y0) in the plane, our goal is to find the shortest distance to
this line.  We can call this distance d (I think on your blackboard we
called it alpha).

To find the shortest distance, we need a straight line that passes
through (x0,y0) and is also perpendicular to the curve f(x).  Let's
define the point where this straight line intersects the curve as
(x1,y1).  This means (comparing slopes)

    (y1 - y0) / (x1 - x0) = -1 / f'(x1)

where f'(x1) is the derivative of f(x) evaluated at the point (x1,y1). 
Rearranging this, and using y1 = f(x1), yields the general result

    f(x1) f'(x1)  -  y0 f'(x1)  +   x1  -   x0  =  0

So, given f(x) and the point (x0,y0), the above is an equation in only
x1.  Solve for x1, use y1 = f(x1), and then the distance d of interest
is given by

    d = sqrt[ (x1 - x0)^2 + (y1 - y0)^2 ]

Example:  suppose we got a reasonable separation of photons and pions
using a curve of the form

    y = f(x) = a + bx^2

Using this in the above general equation yields the cubic equation

    (2b^2) x1^3  +  (2ab + 1 - 2by0) x1  -  x0  =  0

Dividing through by 2b^2, we have an equation of the form

    x^3 + px + q = 0

This can actually be solved analytically - but as I mentioned, a
numerical approach gives us more flexibility to try other forms for the
curve, so this may be the way to go.  I think (haven't proved
rigorously) that for positive values of the constants a, b, x0, and y0,
the cubic will yield three real solutions for x1, but only one will have
x1 > 0, which is the solution of interest.

Anyway, it has been an interesting intellectual exercise!

Scott

I made use of the ROOT function TMath::RootsCubic to solve the cubic equation numerically for computing distances of each point to the curve. With the new quadratic curve f(x)=100+0.1*x^2 the efficiency is 63% and the rejection is 82%.

Efficiency and Rejection

The plot on the left below shows the efficiency of identifying photons over the pt range of 10-30 GeV and the one on the right shows the rejection rate of single neutral pions. Both average about 75% over the pt range of interest.

Rejection vs. efficiency at different energies

The plot below shows background rejection vs. signal efficiency for different energy ranges of the thrown gamma/pi0.

Rejection vs. efficiency with preshower cut

Below on the left is a plot of the ratio of the sum of preshower 1 and 2 to tower energy for both photons (red) and pions (blue). On the right is the rejection of pions vs. efficiency of photons as I cut on the ratio of preshower to tower. It is clear from these plots that the preshower layer is not a good gamma/pi0 discriminator, although can be used to add marginal improvement to the separation preovided by the shower max.

ALL ENERGIES
E=20-40 GeV
E=40-60 GeV
E=60-80 GeV
E=80-90 GeV

---

Pibero Djawotho

Last updated Wed May 30 00:32:16 EDT 2007

2007.06.12 gamma/pi0 separation in EEMC at pT 5-10 GeV

 

gamma/pi0 separation in EEMC at pT 5-10 GeV

---

Pibero Djawotho

Last updated Tue Jun 12 11:59:42 EDT 2007

2007.06.28 Photons in Pythia

2007.07.09 How to run the gamma fitter

 

How to run the gamma fitter

---

The gamma fitter runs out of the box. The code consists of the classes StGammaFitter and StGammaFitterResult in CVS. After checking out a copy of offline/StGammaMaker, cd into the offline directory and run:

root4star StRoot/StGammaMaker/macros/RunGammaFitterDemo.C

The following plots will be generated on the ROOT canvas and dumped into PNG files.

---

Pibero Djawotho

Last modified Mon Jul 9 18:40:07 EDT 2007

2007.07.25 Revised gamma/pi0 algorithm in 2006 p+p collisions at sqrt(s)=200 GeV

 

Revised gamma/pi0 algorithm in 2006 p+p collisions at sqrt(s)=200 GeV

---

Description

The class

StGammaFitter

computes the maximal sided residual of the SMD response in the u- and v-plane for gamma candidates. It is based on C++ code developed by Jason Webb from the original code by Les Bland who got the idea from CDF (?) The algorithm follows the steps below:

1. The SMD response, which is SMD strips with hits in MeV, in each plane (U and V) is stored in histogram hU and hV.
2. Fit functions fU and fV are created. The functional form of the SMD peak is a double-Gaussian with common mean and fixed widths. The widths were obtained by the SMD response of single photons from the EEMC slow simulator. As such, the only free parameters are the common mean and the total yield. The actual formula used is: [0]*(0.69*exp(-0.5*((x-[1])/0.87)**2)/(sqrt(2*pi)*0.87)+0.31*exp(-0.5*((x-[1])/3.3)**2)/(sqrt(2*pi)*3.3))
   • [0] = yield
   • [1] = mean
3. The mean is fixed to the strip with maximum energy and the yield is adjusted so the height of the fit matches that of the mean.
4. The residual for each side of the peak is calculated by subtracting the fit from the data (residual = data - fit) from 2 strips beyond the mean out to 40 strips.
5. The maximal sided residual is the greater residual of each side.

Code

• StGammaFitter.h
• StGammaFitter.cxx
• StGammaFitterResult.h
• StGammaFitterResult.cxx
• ReadGammaTree2.C

Candidates selection

• 2006 p+p at 200 GeV dataset from Sivers analysis (from Jan Balewski)
  /star/institutions/iucf/balewski/prodOfficial06_muDst/
• Gamma candidate from gamma maker: 3x3 clusters with pt > 5 GeV
• No track pointing to cluster
• Minimum of 3 SMD hits in each plane
• Cuts from Jan & Naresh electron analysis:
  • Preshower 1 energy > 0.5 MeV
  • Preshower 2 energy > 2.0 MeV
  • Postshower energy < 0.5 MeV
• The triggers caption in the PDF files shows the trigger id's satisfied by the event. A red trigger id is a L2-gamma trigger. I observe that generally the L2-gamma triggered event are a bit cleaner. Also shown is the pt and energy of the cluster.

Raw SMD response

1. No additional cuts
2. Pick only L2-gamma triggers
3. Pick only L2-gamma triggers but no jet patch trigger
4. Make isolation cut (see below)

The parameters of the isolation cut were suggested by Steve Vigor:

Hi Pibero,

  In general, I believe people have used smaller cone radii for isolation
cuts than for jet reconstruction (where the emphasis is on trying to
recover full jet energy).  So you might try something like requiring
that no more than 10 or 20% of the candidate cluster E_T appears
in scalar sum p_T for tracks and towers within a cone radius of
0.3 surrounding the gamma candidate centroid, excluding the
considered cluster energy.  The cluster may already contain energy
from other jet fragments, but that should be within the purview of
the gamma/pi0 discrimination algo to sort out.  For comparison, Les
used a cone radius of 0.26 for isolation cuts in his original simulations
of gamma/pi0 discrimination with the endcap.  Using much larger
cone radii may lead to accidental removal of too many valid gammas.


Steve

---

Pibero Djawotho

Last updated Wed Jul 25 10:07:07 EDT 2007

2007.09.12 Endcap Electrons

 

Endcap Electrons

---

This analysis is based on the work of Jan and Justin on SMD Profile Analysis for different TPC momenta. See here for a list of cuts. The original code used by Jan and Justin is here.

• Transverse running

• Analysis uses 64 out of 300 runs from 2006 pp transverse run
• MuDst are located at:

  /star/institutions/iucf/balewski/prodOfficial06_muDst/

• No trigger selection


Figure 1: Number of tracks surviving each successive cut



Figure 2a: Number of tracks per trigger id for all electron candidates. Most common trigger ids are:

127652	eemc-jp0-etot-mb-L2jet	EEMC JP > th0 (32, 4 GeV) and ETOT > TH (109, 14 GeV), minbias condition, L2 Jet algorithm, reading out slow detectors, transverse running
127271	eemc-jp1-mb	EEMC JP > th1 (49, 8 GeV) && mb, reading out slow detectors, transverse running
127641	eemc-http-mb-l2gamma	EEMC HT > th1 (12, 2.6 GeV, run < 7100052;13, 2.8 GeV, run >=7100052) and TP > TH1 (17, 3.8 GeV, run < 710052; 21, 4.7 GeV, run>=7100052 ), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 3.4, 5.4, transverse running
127622	bemc-jp0-etot-mb-L2jet	BEMC JP > th0 (42, 4 GeV) and ETOT > TH (109, 14 GeV), minbias condition, L2 Jet algorithm, reading out slow detectors, transverse running; L2jet thresholds at 8.0,3.6,3.3



Figure 2b: Number of tracks per trigger id for all electron candidates for p_T > 4 GeV. The dominant trigger ids become:

127641	eemc-http-mb-l2gamma	EEMC HT > th1 (12, 2.6 GeV, run < 7100052;13, 2.8 GeV, run >=7100052) and TP > TH1 (17, 3.8 GeV, run < 710052; 21, 4.7 GeV, run>=7100052 ), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 3.4, 5.4, transverse running
127262	eemc-ht2-mb-emul	EEMC HT > th2 (22, 5.0 GeV) && mb, reading out slow detectors, emulated in L2, transverse running, different threshold from 117262
127271	eemc-jp1-mb	EEMC JP > th1 (49, 8 GeV) && mb, reading out slow detectors, transverse running
127652	eemc-jp0-etot-mb-L2jet	EEMC JP > th0 (32, 4 GeV) and ETOT > TH (109, 14 GeV), minbias condition, L2 Jet algorithm, reading out slow detectors, transverse running



Figure 3: p_T distribution of tracks before E/p, dE/dx and pT cut



Figure 4: p_T distribution of electron candidates with p_T > 4 GeV



Figure 5: η distribution of electron candidates (all p_T)



Figure 6: φ distribution of electron candidates (all p_T)



Figure 7: dE/dx of tracks before E/p and dE/dx cuts (all p_T)



Figure 8: dE/dx of tracks before E/p and dE/dx cuts (p_T > 4 GeV)



Figure 9: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 0.8 < η < 1.0)



Figure 10: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 1.0 < η < 1.2)



Figure 11: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 1.2 < η < 1.4)



Figure 12: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 1.4 < η < 1.6)



Figure 13: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 1.6 < η < 1.8)



Figure 14: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 1.8 < η < 2.0)

• Click here for SMD profiles of transverse electron candidates.
• Click here for ROOT file with transverse electrons ntuple.


• Longitudinal running

• MuDst are located at:

  /star/institutions/iucf/hew/2006ppLongRuns/
  



Figure 2.1



Figure 2.2: The dominant trigger ids are:

137273	eemc-jp1-mb	EEMC JP > th1 (52, 8.7 GeV) && mb, reading out slow detectors, longitudinal running 2
137641	eemc-http-mb-l2gamma	EEMC HT > th1 (16, 3.5 GeV) and TP > th1 (20, 4.5 GeV), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 3.7, 5.2, longitudinal running 2
137262	eemc-ht2-mb-emul	EEMC HT > th2 (22, 5.0 GeV) && mb, reading out slow detectors, emulated in L2, longitudinal running 2
137222	bemc-jp1-mb	BEMC JP > th1 (60, 8.3 GeV) && mb, reading out slow detectors, longitudinal running 2



Figure 2.3a



Figure 2.3



Figure 2.4



Figure 2.5



Figure 2.6



Figure 2.7



Figure 2.8



Figure 2.9



Figure 2.10

• Click here for SMD profiles of longitudinal electron candidates.
• Click here for ROOT file with longitudinal electrons ntuple.

• Code

Click here for a tarball of the code used in this analysis.

• Summed SMD response

SMD response function

• f(x)=p0*(0.69*exp(-0.5*((x-p1)/0.87)**2)/(sqrt(2*pi)*0.87)+0.31*exp(-0.5*((x-p1)/3.3)**2)/(sqrt(2*pi)*3.3))
  

• p0 = yield

• p1 = centroid

Transverse

21 electrons



Longitudinal

99 electrons

---

Pibero Djawotho

Last updated Wed Sep 12 08:29:53 EDT 2007

2008.01.23 Endcap etas

 

Endcap etas

Endcap etas

This analysis to look for etas at higher energy is in part motivated by this study. The interest in etas, of course, is that their decay photons are well separated at moderate energies (certainly more separated than the photons from pi0 decay). I ran Weihong's pi0 finder with tower seed threshold of 0.8 GeV and SMD seed threshold of 5 MeV (I believe his default SMD seed setting is 2 MeV). I then look in the 2-photon invariant mass region between 0.45 and 0.65 GeV (the PDG nominal mass for the eta is 0.54745 +/- 0.00019 GeV). I observe what looks like a faint eta peak. The dataset processed is the longitudinal 2 run of 2006 from the 20 runs sitting on the IUCF disk in Weihong's directory (/star/institutions/iucf/hew/2006ppLongRuns/).

Within the reconstructed mass window 0.45 to 0.65 GeV, I take a look at the decay photon shower profiles in the SMD. The samples are saved in the file etas.pdf. For the most part, these shower shapes are cleaner than the original sample. Although the statistics are not great.

Additional Material

• List of etas from Weihong (text file)
• List of etas from Weihong (pdf file)
• List of etas from Weihong using gammma maker
• List of etas from Weihong (all 12 sectors)
• Hal's eta spreadsheet
• Hal's revised eta spreadsheet

Documents

• Direct photon center-of-mass angular distributions in proton-antiproton collisions at sqrt(s)=1.8 TeV (Leslie Nakae Thesis)
• Measurement of the cross section for production of two isolated prompt photons in 1.8-TeV proton-antiproton collisions (Mikio Takano Thesis)
• Measurement of sqrt(s) dependence of isolated direct photon production in proton-antiproton collisions (Dana Partos)
• Prompt photon cross section measurement in pbar-p collisions at sqrt(s)=1.8 TeV (PRD 48 2998, 1993)
• A prompt photon cross section measurement in pbar-p collisions at sqrt(s)=1.8 TeV (FERMILAB-Pub-92/001-E)

---

Pibero Djawotho

Last updated Wed Jan 23 12:35:43 EST 2008

2008.02.27 ESMD shape library

 

ESMD shape library

---

Shower Widths for Monte Carlo and Data

Description

Hal did a comparison of the widths of the shower shapes between Monte Carlo and data. Below is a description of what was done.

      I took the nominal central value, either from the maxHit or the
nominal central value, and added the energy in the +/- 12 strips.  Then I
computed the mean strip (which may have been different from the nominal
central value!!).  I normalized the shape to give unit area for each smd
cluster, and added to the histograms separately for U and V and for MC and
data (= Will's events).  I did NOT handle Will's events correctly, just
using whatever event was chosen randomly, rather than going through his
list sequentially.  Note I ran 1000 events, and got 94 events in my shower
shape histos.

      So, there are several minor problems.  1) I didn't go through Will's
events sequentially.  2) I normalized, but perhaps not to the correct 25
strips, because the mean strip and the nominal strip may have differed.
3) there may have been a cutoff on some events due to being close to one
end of the smd plane (near strip 0 or 287).  My sense from looking at the
plots is that these don't matter much.

      The conclusion is that the MC shape is significantly narrower than
the shape from Will's events, which is obviously narrower than the random
clusters we were using at first with no selection for the etas.  Hence, we
are not wasting our time with this project.

• Monte Carlo U shower shape
• Monte Carlo V shower shape
• SMDU shower shape (Will's selection)
• SMDV shower shape (Will's selection)
• Energy Asymmetry
• Index of Library Entry

Decsription of Pythia Sample

A few histograms were added to the code:

• MC is Pythia gamma-jet at partonic pT 9-11 GeV with gamma in the Endcap
• Data is from Will Jacobs golden events from Weihong sample
• Require no conversion
• Require all hits from direct photon in same sector

Figure 1:

Data vs. MC mean u-strip

Figure 2:

Data vs. MC mean v-strip

Figure 3:

Data vs. MC u-strip sigma

Figure 4:

Data vs. MC v-strip sigma

Figure 5:

MC E

_v

vs. E

_u

Figure 6:

Data E

_v

vs. E

_u

Figure 7:

MC energy asymmetry in SMD planes

Figure 8:

Data energy asymmetry in SMD planes

Figure 9:

Shower shape library index used (picked at random)

 

Single events shower shapes are displayed in

esmd.pdf

or

esmd_solid.pdf

.

• green = projected position of direct photon in the Endcap
• blue = Monte Carlo SMD response
• red = Data SMD response

---

Hal Spinka

Pibero Djawotho

Last modified Wed Feb 27 09:51:27 EST 2008

2008.02.28 ESMD QA for run 7136033

 

ESMD QA for run 7136033

---

---

Pibero Djawotho

Last updated Thu Feb 28 16:17:55 EST 2008

2008.03.04 A second look at eta mesons in the STAR Endcap Calorimeter

 

A second look at eta mesons in the STAR Endcap Calorimeter

---

Introduction

In case you missed it, the first look is

here

. I processed

44 runs

from the 2006 pp longitudinal 2 runs and picked events tagged with the L2gamma trigger id (137641). I ran the StGammaMaker on the MuDst files from these runs and produced gamma trees. These gamma trees are available at

/star/institutions/iucf/pibero/2007/etaLong/

. Within the StGammaMaker framework, I developed code to seek candidate etas with emphasis on high purity. The macros and source files are:

• Eta.C
• StEtaFinder.h
• StEtaFinder.cxx
• PlotEtas.C
• ETAS.root

Note, the workhorse function is

StEtaFinder::findTowerPoints()

.

Algorithm

1. Find seed tower with pT > 0.8 GeV
2. Require no TPC track into the seed tower
3. Get the ranges of SMD U & V strips that span the volume of the seed tower
4. Find the strips with maximum energy within these ranges
5. Require that the maximum strips have more than 2 MeV in each SMD plane
6. Get the intersection of the maximum strips and ensures that it lies within 70% of the fiducial volume of the seed tower
7. Make sure the photon candidate responsible for the SMD clusters above enters and exits the same seed tower
8. Form a 11-strip cluster in each plane with +/-5 strips around the max strip and require that it contains 70% of the energy in a range +/-20 strips around the max strip
9. Require that the energy asymmetry between the 11-strip clusters in the U and V planes be less than 20%
10. Create a point using the energy of the seed tower and the position of the intersection of the max strips in the SMD U and V planes
11. Repeat until seed towers in the event are exhausted
12. Combine different points in the event to calculate the invariant mass
13. Diphoton pairs with invariant mass between 0.4 and 0.6 GeV are saved to a PDF file

Invariant mass

I fit the diphoton invariant mass with two Gaussians, one for the pi0 peak (p0-p2) and another one for the eta peak (p3-p5) plus a quadratic for the background (p6-p8). The Gaussian is of the form p0*exp(0.5*((x-p1)/p2)**2) and the quadratic is of the form p6*+p7*x+p8*x**2. A slightly better chi2/ndf in the fit is achieved by using Breit-Wigner functions instead of Gaussians for the signal here. I calculate the raw yield of etas from the fit as p3*sqrt(2*pi)*p5/bin_width = 85 where each bin is 0.010 GeV wide. I select candidate etas in the mass range 0.45 to 0.55 GeV and plot their photon response in the shower maximum detector here. Since we are interested in collecting photons of pT > 7 GeV, only those candidate photons with pT > 5 GeV will be used in the shower shape library. I also calculate the background under the signal region by integrating the background fit from 0.45 to 0.55 GeV and get 82 counts.

• S = 85
• B = 82
• S:B = 1.03:1
• S/√S+B = 6.6

Additional plots

 

• PhotonsFromEtas.txt
• PhotonsFromEtas2.txt

---

Pibero Djawotho
Last updated at Tue Mar 4 16:21:13 EST 2008

2008.03.08 Adding the SMD energy to E_reco/E_MC for Photons

 

Adding the SMD energy to E_reco/E_MC for Photons

The following is a revisited study of E_reco/E_MC for photons with the addition of the SMD energy to E_reco.

QA plots for each energy

1. 5 GeV
2. 10 GeV
3. 20 GeV
4. 40 GeV
5. 80 GeV
6. 160 GeV

E_SMD/E_reco vs. eta

---

Pibero Djawotho

Last updated Thu Mar 8 04:27:28 EST 2007

2008.03.21 Chi square method

Chi square method

 

 SectorVsRunNumber.png   10-Feb-2010 12:22   14K  
 ShowerShapes.png        10-Feb-2010 12:22   17K  
 chiSquareMC.png         10-Feb-2010 12:22   13K  
 chiSquarePibero.png     10-Feb-2010 12:22   16K  
 chiSquareWill.png       10-Feb-2010 12:22   16K  
 chiSquareWillAndMC.png  10-Feb-2010 12:22   17K  

 

2008.04.08 Data-Driven Shower Shapes

 

Data-Driven Shower Shapes

---

Gamma Conversion before the Endcap

The plots below show the conversion process before the Endcap. I look at prompt photons heading towards the Endcap from a MC gamma-jet sample with a partonic pT of 9-11 GeV. I identify those photons that convert using the GEANT record. The top left plot shows the total number of direct photons and those that convert. I register a 16% conversion rate. This is consistent with Jason's 2006 SVT review. The top right plot shows the source of conversion, where most of the conversions emanate from the SVT support cone, also consistent with Jason's study. The bottom left plot shows the separation in the SMD between the projected location of the photon and the location of the electron/positron from conversion.

Shower shapes comparison

This

PDF

file shows several shower shapes in a single plot for comparison:

• MC - Monte Carlo shower shape from the 9-11 GeV pT gamma-jet Pythia sample
• DD - Data-driven Monte Carlo shower shape (Each final state photon shower shape is replaced with a corresponding shower shape from data in the same sector configuration, energy, preshower, and U/V-plane bin).
• Standard MC - Monte Carlo shower shape parametrized by Hal (also from the 9-11 GeV pT gamma-jet Pythia sample)
• Will - Data shower shape derived from photons from eta decays by Will using a modified version of Weihong/Jason meson pi0 finder
• Pibero - Data shower shape derived from photons from eta decays by Pibero using a crude eta finder

Shower Shapes Sorted by SMD Plane, Sector Configuration, Energy and Preshower

These Shower Shapes are binned by:

1. SMD plane (U and V)
2. Sector configuration with the formula sector%3 where sector=1..12, so 3 different bins. More details can be found at the EEMC Web site under the Geometry link.
3. Energy of the photon (E < 8 GeV and E > 8 GeV)
4. Preshower energy (pre1==0&&pre2==0) and (pre1>0||pre2>0)

They are then fitted with a triple-Gaussian of the form:

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

Comparison of Sided Residuals for Monte Carlo (MC) and Data-Driven (DD) Shower Shapes

All fits to MC are with reference to the old Monte carlo fit function:

[0]*(0.69*exp(-0.5*((x-[1])/0.87)**2)/(sqrt(2*pi)*0.87)+0.31*exp(-0.5*((x-[1])/3.3)**2)/(sqrt(2*pi)*3.3))

All fits to the data are with reference to a single

Shower Shape

. The fit function is:

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

1. All Shower Shapes
2. No Conversion
3. Conversion
4. No Preshower
5. Preshower
6. No Conversion and Preshower
7. Sector Configuration 0
8. Sector Configuration 1
9. Sector Configuration 2

Comparison of Sided Raw Tails for Monte Carlo (MC) and Data-Driven (DD) Shower Shapes

1. All Shower Shapes
2. No Conversion
3. Conversion
4. No Preshower
5. Preshower
6. No Conversion and Preshower
7. Sector Configuration 0
8. Sector Configuration 1
9. Sector Configuration 2

---

Pibero Djawotho

Last updated Tue Apr 8 17:29:40 EDT 2008

2008.04.12 Data-Driven Residuals

 

Data-Driven Residuals

---

Gammas

Jets

Background Rejection vs. Signal Efficiency

Partonic pT=9-11 GeV	Partonic pT=9-11 GeV

Background Rejection vs. Signal Efficiency (Neutral Meson pT > 8 GeV)

---

Pibero Djawotho

Last updated Sat Apr 12 13:27:50 EDT 2008

2008.04.12 Pythia Gamma-Jets

 

Pythia Gamma-Jets

---

Gamma-Jet Yields

During Run 6, the L2-gamma trigger (trigger id 137641) sampled 4717.10 nb^-1 of integrated luminosity. By restricting the jet to the Barrel, |η_jet|<1, and the gamma to the Endcap, 1<η_gamma<2, the yield of gamma-jets is estimated as the product of the luminosity, the cross section, and the fraction of events in the phasespace above. The total cross section reported by Pythia for gamma-jet processes at different partonic p_T thresholds is listed in the table below. No efficiencies are included.

pT threshold [GeV]	Total cross section [mb]	Fraction	Ngamma-jets
5	6.551E-05	0.0992	30654
6	3.075E-05	0.1161	16840
7	1.567E-05	0.1150	8500
8	8.654E-06	0.1131	4617
9	4.971E-06	0.1223	2868
10	2.953E-06	0.1151	1603

Gamma-Jets p_T slope

The p_T slope is exp(-0.69*pT)=2^(-pT), so the statistics are halved with each 1 GeV increase in p_T.

References

1. Yield estimates based on single-particle MC sample, and comparison w/ pythia (Jason Webb)
2. Pythia estimates of gamma-jet yields (Jim Sowinski)

---

Pibero Djawotho

Last updated Sat Apr 12 15:15:56 EDT 2008

2008.04.16 Jet Finder QA

 

Jet Finder QA

Pibero Djawotho

Last updated Wed Apr 16 08:33:01 EDT 2008

2008.04.20 BUR 2009

Partonic pT=7-9 GeV

Partonic pT=9-11 GeV

Combined Partonic pT

2008.04.22 Run 6 Photon Yield Per Trigger

 

Run 6 Photon Yield Per Trigger

---

Introduction

The purpose of this study is to estimate the photon yield per trigger in the Endcap Electromagnetic Calorimeter during Run 6. The trigger of interest is the L2-gamma trigger. Details of the STAR triggers during Run 6 were compiled in the 2006 p+p run (run 6) Trigger FAQ by Jamie Dunlop. The triggers relevant to this study are reproduced in the table below for convenience.

Trigger id	Trigger name	Description
117641	eemc-http-mb-l2gamma	EEMC HT > th1 (12, 2.6 GeV) and TP > TH1 (17, 3.8 GeV), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 2.9, 4.5
127641	eemc-http-mb-l2gamma	EEMC HT > th1 (12, 2.6 GeV, run < 7100052;13, 2.8 GeV, run >=7100052) and TP > TH1 (17, 3.8 GeV, run < 710052; 21, 4.7 GeV, run>=7100052 ), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 3.4, 5.4, transverse running
137641	eemc-http-mb-l2gamma	EEMC HT > th1 (16, 3.5 GeV) and TP > th1 (20, 4.5 GeV), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 3.7, 5.2, longitudinal running 2

The luminosity sampled by each trigger was also caclulated here by Jamie Dunlop. The luminosity for the relevant triggers is reproduced in the table below for convenience. The figure-of-merit (FOM) is calculated as FOM=Luminosity*P_B*P_Y for transverse runs and FOM=Luminosity*P_B²*P_Y² for longitudinal runs where P_B is the polarization of the blue beam and P_Y is the polarization of the yellow beam. Naturally, in spin physics, the FOM is the better indicator of statistical precisison.

Trigger	First run	Last run	Luminosity [nb-1]	Figure-of-merit [nb-1]
117641	7093102	7096017	118.88	11.89
127641	7097009	7129065	3219.04	1099.43
137641	7135050	7156028	4717.10	687.65

Event selection

Trigger selection

For this study, only the trigger of longitudinal running 2 (137641) is used. As mentioned above, at level-0, an EEMC high tower above 3.5 GeV and its associated trigger patch above 4.5 GeV in transverse energy coupled with a minimum bias condition, which is simply a BBC coincidence to ensure a valid collision, is required for the trigger to fire. The EEMC has trigger patches of variable sizes depending on their location in pseudorapidity. (The BEMC has trigger patches of fixed sizes, 4x4 towers.) At level-2, a high tower above 3.7 GeV and a 3x3 patch above 5.2 GeV in transverse energy is required to accept the event.

Gamma candidates

In addition to selecting events that were tagged online by the L2-gamma trigger, the offline

StGammaMaker

looks for tower clusters with minimum transverse energy of 5 GeV. These clusters along with their associated TPC tracks, preshower and postshower tiles, and SMD strips form gamma candidates. Gamma trees for the 2006 trigger ID 137641 with primary vertex are located at

/star/institutions/iucf/pibero/2006/gammaTrees/

.

Track isolation

The gamma candidate is required to have no track pointing to any of its towers.

EMC isolation

The gamma candidate is required to have 85% of the total transverse energy in a cone of radius 0.3 in eta-phi space around the position of the gamma candidate. That is E

_T^gamma

/E

_T^cone

> 0.85 and R=√Δη

²

+Δφ

²

=0.3 is the cone radius.

Jet Reconstruction

The gamma candidate is matched to the best away-side jet with neutral fraction < 0.9 and cos(φ

_gamma

-φ

_jet

) < -0.8. The 2006 jet trees are produced by Murad Sarsour at PDSF in

/eliza13/starprod/jetTrees/2006/trees/

. A local mirror exists at RCF under the directory

/star/institutions/iucf/pibero/2006/jetTrees/

.

Spin Information

Jan Balewski has an excellent write-up, Offline spin DB at STAR, on how to get spin states. I obtain the spin states from the skim trees in the jet trees directory. In brief, the useful spin states are:

Blue Beam Polarization	Yellow Beam Polarization	Spin4
P	P	5
P	N	6
N	P	9
N	N	10

Event Summary

L2-gamma triggers	730128
Endcap gamma candidates	723848
Track isolation	246670
EMC isolation	225400
Away-side jet	99652
SMD max sided residual	19281
Barrel-only jet	15638

Note the number of L2-gamma triggers include only those events with a primary vertex and at least one gamma candidate (BEMC or EEMC).

Gamma-Jet Plots

Comparison of pT Slope with Pythia

Partonic Kinematics Reconstruction

Open Questions

1. I count ~2.4M events with trigger id 137641 using the Run 6 Browser, however my analysis only registers about ~0.78M.

---

Pibero Djawotho

Last updated Tue Apr 22 11:40:18 EDT 2008

2008.05.07 Number of Jets

 

Number of Jets

---

After selecting Endcap gamma candidates out of L2-gamma triggers, applying track and EMC isolation cuts, and matching the Endcap gamma candidate to an away-side jet, I record the number of jets below per event. Surprisingly, 8% of the events only have 1 jet. Those are events where the Endcap gamma candidate was not reconstructed as a neutral jet by the jet finder. The question is why.

I display both Barrel and Endcap calorimeter towers (the z-axis represents tower energy) and draw a circle of radius 0.3 around the gamma candidate and a circle of radius 0.7 around the away-side jet for 2006 pp200 run 7136022. Even though many of the gamma candidates not reconstructed by the jet finder are at the forward edge of the Endcap, it is not at all clear why those that are well within the detector are not being reconstructed.

---

Pibero Djawotho

Last updated Wed May 7 09:54:32 EDT 2008

2008.05.09 Gamma-jets pT distributions

 

Gamma-jets pT distributions

---

Note:

No cuts on residuals applied.

Not cut on number of towers in gamma cluster

Number of towers in gamma cluster <= 9

Number of towers in gamma cluster <= 4

Number of towers per cluster distributions

Gamma candidates xy-distribution

z-vertex distribution

Eta distribution

Phi distribution

log10(E_post/E_tow) distribution

pT asymmetry

References

1. Ilya's pT distributions
2. Michael's weigthing of simulation

---

Pibero Djawotho

Last updated Fri May 9 08:19:00 EDT 2008

2008.05.19 Binning the shower shape library

 

Binning the shower shape library

---

Distributions

Shower Shapes

---

Pibero Djawotho

Last updated Mon May 19 12:09:48 EDT 2008

2008.06.03 Jet A_LL Systematics

 

Jet A_LL Systematics

---

Hypernews discussion

jet A_LL systematic possibility

References

1. I.P. Auer et al, Phys.Rev.D 32(1985)1609
2. J. Bystricky et al, J.Phys. France 39(1978)1

---

Pibero Djawotho

Last updated Tue Jun 3 15:35:24 EDT 2008

2008.06.18 Photon-jet reconstruction with the EEMC - Part 2 (STAR Collaboration Meeting - UC Davis)

2008.07.16 Extracting A_LL and DeltaG

 

Extracting A_LL and DeltaG

---

Determining state of beam polarization for Monte Carlo events

While Pythia does a pretty good job of simulating prompt photon production in p+p collisions, it does not include polarization for the colliding protons nor partons. A statistical method for assigning polarization states for each event based on A_LL [1] is demonstrated in this section. For an average number of interactions for each unpolarized bunch crossing, N_eff, the occurence of an event with a particular polarization state obeys a Poisson distribution with average yield of events per bunch crossing:

For simplicity, the polarizations of the blue and yellow beams are assumed to be P

_B

=P

_Y

=0.7 and N

_eff

=0.01. The "+" spin state defines the case where both beams have the same helicities and the "-" spin state for the case of opposite helicities. The asymmetry A

_LL

is calculated from the initial states polarized and unpolarized parton distribution functions and parton-level asymmetry:

The algorithm then consists in alternatively drawing a random value N

_int

from the Poisson distributions with mean μ

₊

and μ

_-

until N

_int

>0 at which point an interaction has occured and the event is assigned the current spin state. The functioning of the algorithm is illustrated in Figure 1a where an input A

_LL

=0.2 was fixed and N

_trials

=500 different asymmetries were calculated. Each trial integrated N

_total

=300 events. It is then expected that the mean A

_LL

~0.2 and the statistical precision~0.1:

Indeed, both the A

_LL

and its error are reproduced. In addition, variations on the number of events per trial were investigated (N

_total

) in Figure 1b. The extracted width of the Gaussian distribution for A

_LL

is consistent with the prediction for the error (red curve).

• ROOT macro used to generate Figure 1a SimALL.C
• ROOT macro used to generate Figure 1b SimALL2.C

Figure 1a	Figure 1b

Event reconstruction

For this study, the gamma-jets Monte Carlo sample for all partonic p_T were used. As an example, the prompt photon processes for the partonic p_T bin 9-11 GeV and their total cross sections are listed in the table below. Each partonic p_T bin was divided into 15 files each of 2000 events.

 ==============================================================================
 I                                  I                            I            I
 I            Subprocess            I      Number of points      I    Sigma   I
 I                                  I                            I            I
 I----------------------------------I----------------------------I    (mb)    I
 I                                  I                            I            I
 I N:o Type                         I    Generated         Tried I            I
 I                                  I                            I            I
 ==============================================================================
 I                                  I                            I            I
 I   0 All included subprocesses    I         2000          9365 I  3.074E-06 I
 I  14 f + fbar -> g + gamma        I          331          1337 I  4.930E-07 I
 I  18 f + fbar -> gamma + gamma    I            2             8 I  1.941E-09 I
 I  29 f + g -> f + gamma           I         1667          8019 I  2.579E-06 I
 I 114 g + g -> gamma + gamma       I            0             1 I  1.191E-10 I
 I 115 g + g -> g + gamma           I            0             0 I  0.000E+00 I
 I                                  I                            I            I
 ==============================================================================

The cross sections for the different partonic p_T bins has been tabulated by Michael Betancourt and is reproduced here for convenience.

Partonic pT [GeV]	Cross Section [mb]
3-4	0.0002962
4-5	0.0000891
5-7	0.0000494
7-9	0.0000110
9-11	0.00000314
11-15	0.00000149
15-25	0.000000317
25-35	0.00000000990
35-45	0.000000000449

These events were processed through the 2006 pp200 analysis chain, albeit without any cuts on the SMD. The simulated quantities were taken from the Pythia record and the reconstructed ones from the analysis.

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Partonic kinematics reconstruction

PartonicKinematics.C

Figure 10

Figure 11

Figure 12

Figure 13

Figure 14

Figure 15 (a)

Figure 15 (b): quark from proton 1 (blue beam, +z direction)

Figure 15 (c): quark from proton 1 (blue beam, +z direction) and q + g -> q + gamma subprocess (29)

Figure 15 (d): quark from proton 1 (blue beam, +z direction) and q + qbar -> gamma + g subprocess (14)

Predictions for A_LL and direct determination of DeltaG(x)

DeltaG.C

Figure 16 (a)

Figure 16 (b): quark from proton 1 (blue beam, +z direction)

Figure 16 (c): quark from proton 1 (blue beam, +z direction) and q + g -> q + gamma subprocess (29)

Figure 16 (d): quark from proton 1 (blue beam, +z direction) and q + qbar -> gamma + g subprocess (14)

References

1. Appendix Simulation Studies of Direct Photon Production at STAR
2. DeltaG(x,mu^2) from jet and prompt photon production at RHIC arXiv:hep-ph/0005320

---

Pibero Djawotho

Last updated Wed Jul 16 10:29:22 EDT 2008