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E. Soldatov

Tight photon efficiency study using FSR photons from Z  decay. E.Yu.Soldatov* *National Research Nuclear University “MEPhI”. Outline: MC study of the chosen sample Background estimation study Tight cut study. E. Soldatov. Photon ID efficiencies meeting. 01 . 09 .2011. Introduction.

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E. Soldatov

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  1. Tight photon efficiency study using FSR photons fromZ decay E.Yu.Soldatov* *National Research Nuclear University “MEPhI” Outline: • MC study of the chosen sample • Background estimation study • Tight cut study E. Soldatov Photon ID efficiencies meeting 01.09.2011

  2. Introduction A kinematic approach of the photon selection Three body mass spectrum (Z) Signal -FSR Background -ISR -Brem -Jets Kinematics approach is based on a simple criteria of the event selection. 1. Mass of two leptons should have a missing part e.g. < Z bozon mass For electron channel 60 < m(ee) < 83 GeV, and for muon channel 40 < m() < 82 GeV 2. And 3 body mass should correspond to a mass of Z bozon For electron channel 80 < m(ee)< 94 GeV and for muon channel 81 < m() < 95 GeV E. Soldatov Photon ID efficiencies meeting 01.09.2011 №2

  3. Introduction What do we want? The main idea is to obtain a photon sample with maximum purity with the method decoupled from the standard analysis methods. Why do we want this? Studies of the ATLAS detector performance with tagged photons and first of all study and optimization of the tight cut criteria. Very important for analysis all photon containing processes. The best source of such kind of photons is a production of FSR photons from Z-lepton decay: A “minor” problem: a small production cross section. E. Soldatov Photon ID efficiencies meeting 01.09.2011 №3

  4. Photon purity after all cuts applied Purity of the sample after Et (in cone 0.2)<3 GeV and N=0 cuts Purity=Signal/(Signal+Background) Differential purity for each bin. Et cut [GeV] Figure. Purity of the sample as a function of photon energy E. Soldatov Photon ID efficiencies meeting 01.09.2011 №4

  5. Tightcut efficiency study Efficiency of robust tight cut on signal (lift) and on background (right) vs Et photon spectrum, Et (in cone 0.2)<3 GeV - Sgn sample MC - Data 2011 - Bkg sample MC - Data 2011   Signal Background ET [GeV] ET [GeV] Tight CUT efficiency for signal sample Tight CUT efficiency for background sample E. Soldatov Photon ID efficiencies meeting 01.09.2011 №5

  6. Tightcut efficiency study Efficiency of robust tight cut on signal (lift) and on background (right) vs Et photon spectrum, Et (in cone 0.2)<3 GeV (barrel only) Signal Background ET [GeV] ET [GeV] Tight CUT efficiency for signal sample Tight CUT efficiency for background sample E. Soldatov Photon ID efficiencies meeting 01.09.2011 №6

  7. Tightcut efficiency study Efficiency of robust tight cut on signal (lift) and on background (right) vs Et photon spectrum, Et (in cone 0.2)<3 GeV Signal Background ET [GeV] ET [GeV] Tight CUT efficiency for signal sample Tight CUT efficiency for background sample E. Soldatov Photon ID efficiencies meeting 01.09.2011 №7

  8. Tightcut efficiency study Multiplicity for data E. Soldatov Photon ID efficiencies meeting 01.09.2011 №8

  9. Tightcut efficiency study For multiplicity<5 E. Soldatov Photon ID efficiencies meeting 01.09.2011 №9

  10. Tightcut efficiency study For multiplicity>6 E. Soldatov Photon ID efficiencies meeting 01.09.2011 №10

  11. Effect of background uncertainty on the tight cut efficiency Background estimation from MC 5-10 GeV 15-20 GeV MC Purity=0.827 (slide 11) S+B Efficiency=0.17 (sl.20) Bkg Efficiency=0.06 (sl.20) MC Purity=0.973 (slide 11) S+B Efficiency=0.53 (sl.20) Bkg Efficiency=0.32 (sl.20) S Efficiency=(0.17-0.06*0.173)/0.827=0.193 S Efficiency=(0.53-0.32*0.027)/0.973=0.5358 Correction gives us uncertainty: (0.193-0.17)/0.17*100%=13.5% Correction gives us uncertainty: (0.5358-0.53)/0.53*100%=1% E. Soldatov Photon ID efficiencies meeting 01.09.2011 №11

  12. Data driven background estimation Work of the background estimation method on real data REAL DATA Cut Et (in cone 0.2)<3 GeV ET()>5 GeV, ET()<10 GeV N (tracks in cone20) First bin: Data=2312; Bkg estimation=853 => Purity=0.631 E. Soldatov Photon ID efficiencies meeting 01.09.2011 №12

  13. Data driven background estimation Work of the background estimation method on real data REAL DATA Cut Et (in cone 0.2)<3 GeV ET()>15 GeV, ET()<20 GeV N (tracks in cone20) First bin: Data=774; Bkg estimation=66 => Purity=0.915 E. Soldatov Photon ID efficiencies meeting 01.09.2011 №13

  14. Effect of background uncertainty on the tight cut efficiency Background estimate – data driven 5-10 GeV 15-20 GeV Estimated Purity=0.631 (sl.22) S+B Efficiency=0.17 (sl.20) Bkg Efficiency=0.06 (sl.20) Estimated Purity=0.915 (sl.23) S+B Efficiency=0.53 (sl.20) Bkg Efficiency=0.32 (sl.20) S Efficiency=(0.17-0.06*0.369)/0.631=0.234 Correction gives us uncertainty: (0.234-0.17)/0.17*100%=38% Uncertainty due to the method: (0.234-0.193)/0.193*100%=21% S Efficiency=(0.53-0.32*0.085)/0.915=0.5495 Correction gives us uncertainty: (0.5495-0.53)/0.53*100%=3.7% Uncertainty due to the method: (0.5495-0.5358)/0.5358*100%=2.5% E. Soldatov Photon ID efficiencies meeting 01.09.2011 №14

  15. Tightcut efficiency study (Z->ee) Efficiency of robust tight cut on signal (lift) and on background (right) vs Et photon spectrum, Et (in cone 0.2)<3 GeV electron channel Signal Background ET [GeV] ET [GeV] Tight CUT efficiency for signal sample Tight CUT efficiency for background sample E. Soldatov Photon ID efficiencies meeting 01.09.2011 №15

  16. Photon purity after all cuts applied (Z->ee) Purity of the sample after Et (in cone 0.2)<3 GeV and N=0 cuts electron channel Estimated purity from here: MC purity of the sample: ET()>5 GeV Et cut [GeV] Data driven estimation of the background amount in the first bin ET()>15 GeV E. Soldatov Photon ID efficiencies meeting 01.09.2011 №16

  17. Effect of background uncertainty on the tight cut efficiency (Z->ee) Background estimate – MC&data driven electron channel 5-10 GeV 15-20 GeV MC Purity=0.96 (slide 26) Estimated Purity=0.909 (sl.26) S+B Efficiency=0.55 (sl.25) Bkg Efficiency=0.26 (sl.25) MC Purity=0.593 (slide 26) Estimated Purity=0.582 (sl.26) S+B Efficiency=0.15 (sl.25) Bkg Efficiency=0.06 (sl.25) S Eff (MC pur)=(0.15-0.06*0.407)/0.593=0.212 S Eff (Est pur)=(0.15-0.06*0.418)/0.582=0.215 Uncertainty due to the method: (0.215-0.212)/0.212*100%=1.4% S Eff (MC pur)=(0.55-0.26*0.04)/0.96=0.562 S Eff (MC est)=(0.55-0.26*0.091)/0.909=0.579 Uncertainty due to the method: (0.579-0.562)/0.562*100%=3% E. Soldatov Photon ID efficiencies meeting 01.09.2011 №17

  18. Conclusions The improvement of signal selection has been done. The purity became larger than 97% for 15 GeV photon sample. Monte Carlo simulation has been compared with the 2011 Data (periods D-I with ~1.33fb-1 of integral luminocity). To improve the agreement between simulation and data background estimation method from data has been proposed and implemented. Tight cut efficiency has been estimated using two methods of sample purity estimation. Efficiency from data has significant differences from MC. E. Soldatov Photon ID efficiencies meeting 01.09.2011 №18

  19. Back-up slides E. Soldatov Photon ID efficiencies meeting 01.09.2011 №19

  20. Data driven background estimation Crosscheck on a larger statistics! ET()>5 GeV Excellent agreement! In order to obtain more background photons under in a signal sample less stringent kinematic conditions where taken: 40 < m() < 88 GeV 75 < m() < 105 GeV N (tracks in cone20) Number of tracks in cone 0.2 around photon vector in ID. Again the black line shows the background estimate using extrapolation method described above. E. Soldatov Photon ID efficiencies meeting 01.09.2011 №20

  21. MC: Study of the pure background sample. Background photon spectrums Cut Et (in cone 0.2)<3 GeV Applying 3 body mass cut we deform a bit the photon spectrum. Does it affect the background estimation? ET [GeV] Spectrum of background photons from the signal sample after 3 body mass CUT applied Spectrum of background photons from the background sample E. Soldatov Physics in ATLAS 28.07.2011 №21

  22. MC study: Pt cone and N tracks (cone 0.2) Background sample extraction will provide us exact distribution of ID track information for background photon candidates Background sample kinematic cuts are: for muon channel 89 < m() < 93 GeV Cut Et (in cone 0.2)<3 GeV ET()>10 GeV PT(cone20) [GeV] N (tracks in cone20) Sum of all momentums in cone 0.2 around photon vector in ID Number of tracks in cone 0.2 around photon vector in ID E. Soldatov Physics in ATLAS 28.07.2011 №22

  23. MC study: Pt cone and N tracks (cone 0.2) Background sample extraction will provide us exact distribution of ID track information for background photon candidates Background sample kinematic cuts are: for muon channel 89 < m() < 93 GeV Cut Et (in cone 0.2)<3 GeV ET()>15 GeV We have almost the same picture for all energies. But for high energies, we have a bit large fraction of the signal. PT(cone20) [GeV] N (tracks in cone20) Sum of all momentums in cone 0.2 around photon vector in ID Number of tracks in cone 0.2 around photon vector in ID E. Soldatov Physics in ATLAS 28.07.2011 №23

  24. MC: Study of the pure background sample. Comparison of the track number distribution for different background photon samples Cut Et (in cone 0.2)<3 GeV ET()>10 GeV Distribution of number of tracks in the cone 0.2 inInner Detector around background photon (black line histogram) in the signal sample the three-body invariant mass cut applied, and for photon background sample (blue filled histogram) E. Soldatov Physics in ATLAS 28.07.2011 №24

  25. MC: Study of the pure background sample. Comparison of the track number distribution for different background photon samples Cut Et (in cone 0.2)<3 GeV The shapes of the distribution are in a less good agreement, than for small energies. However due to big statistical errors, we can use this shape from photon background sample for a data driven background estimate normalizing on number of events for N>1. There is no signal in this area ET()>15 GeV Distribution of number of tracks in the cone 0.2 inInner Detector around background photon (black line histogram) in the signal sample the three-body invariant mass cut applied, and for photon background sample (blue filled histogram) E. Soldatov Physics in ATLAS 28.07.2011 №25

  26. Data driven background estimation Signal to background ratio is much higher for larger photon energies Always condition Et in cone20 < 3 GeV is used ET>10 GeV We use these bins for extrapolation Relaxed conditions: 40<m()<88 GeV 75<m()<105 GeV (to gain more bkg statistics) We have a problem: signal here! N (tracks in cone20) N (tracks in cone20) Number of tracks in cone 0.2 around photon vector in ID. Again the black line shows the background estimate using extrapolation method described above. E. Soldatov Physics in ATLAS 28.07.2011 №26

  27. Tightcut effect on background sample Efficiency of robust tight cut on background vssum of Et in cone 0.2 in calorimeter around photon (left) and Et photon spectrum (right), Et(in cone20)<3 GeV (one photon candidate in event) List all the conditions: all kinematic cuts, Et in cone20 < 3 GeV , N tracks = 0 ET [GeV] Distribution of Et photon spectrumfor background sample before (left) and after (right) robust tight photon cut. ET(cone20) [GeV] E. Soldatov Physics in ATLAS 28.07.2011 №27

  28. Tightcut effect on background sample List all the conditions: all kinematic cuts, Et in cone20 < 3 GeV , N tracks = 0 • N=Ns+Nb=17228 • after tight cut we have • Nt=effs*Ns+effb*Nb=8414 • We also know purity=Ns/N (slide 9) • We can extract pure effs (we know effb from slide 25): Effs=(Nt-effb*Nb)/Ns=Nt/N*purity-effb*(1/purity-1)=0.38 (for 5 GeV) E. Soldatov Physics in ATLAS 28.07.2011 №28

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