Slide 1 SMP-V: Report for Luminosity Calibration Study Jiyeon Han, Ping Tan Alexey Svyatkovskiy, Hwidong Yoo Stoyan Stoynev Ilya Kravchenko, Jamila Butt Youn Roh, Efe Yazgan

Slide 2 Introduction Significant luminosity calibration issue with 2011 dataset is introduced from Luminosity group – d=slides&confId=169386https://indico.cern.ch/getFile.py/access?contribId=2&resId=0&materialI d=slides&confId= –About 7.5% uncertainty SMP-V group organizes the task force team to investigate the issue using Z  mumu and Z  ee events –Single ISO muon trigger based Based on muon charge asymmetry analysis –Double muon trigger based Based on Drell-Yan measurement –Double electron trigger based Based on Drell_Yan & AFB measurements Products –Number of Z candidates as a function of run period –Efficiency as a function of run period / number of vertices –Z yield corrected by efficiency –Absolute Z cross section –Predicted luminosity using Z cross section (using NNLO Z cross section)

Slide 3 lumiCalc2.py We use 3 different version of lumiCalc2.py –Plain version: default described in Will call as v1 in this talk –v2: updated in last week. It is available in HEAD --correctionv2 –v3: updated yesterday. It needs to change in scripts manually

Slide 4 Single Iso-Mu Trigger Provided by Jiyeon Han, Ping Tan

Slide 5 Samples & Event Selection Samples –Runs_ : Run2011A-May10ReReco-v1_AOD –Runs_ _DS_SingleMu_Run2011A-May10ReReco-v1_AOD –Runs_ _DS_SingleMu_Run2011A-PromptReco-v4_AOD –Runs_ _DS_SingleMu_Run2011A-PromptReco-v4_AOD –Runs_ _DS_SingleMu_Run2011A-PromptReco-v4_AOD –Runs_ _DS_SingleMu_Run2011A-05Aug2011-v1_AOD –Runs_ _DS_SingleMu_Run2011A-PromptReco-v6_AOD –Runs_ _DS_SingleMu_Run2011A-PromptReco-v6_AOD –Runs_ _DS_SingleMu_Run2011B-PromptReco-v1_AOD –Runs_ _DS_SingleMu_Run2011B-PromptReco-v1_AOD –Runs_ _DS_SingleMu_Run2011B-PromptReco-v1_AOD Trigger –HLT_IsoMu15 up to run = –HLT_IsoMu24 for 2011A data set above run = –HLT_IsoMu30 for 2011B data set Selection –VBTF baseline selection –Slide 3: &confId=167985

Slide 6 Run Period Run period 1 : a. 5E32; May10; run range ( ) Run period 2 : b. 1.4E33; PromptV4; run range ( ) Run period 3 : c. 2E33; Aug05+PromptV6; run range ( ) Run period 4 : d. 3E33; PromptV6; run range ( ) Run period 5 : e. 3E33(higher PU); 2011B; run range ( ) Run period 6 : f. 5E33; 2011B; run range ( )

Slide 7 Efficiencies Single muon object efficiency as a function of run period and number of vertices Trigger efficiency Reco+ID efficiency

Slide 8 N (events) / Efficiencies Efficiency corrected number of events which we can check the trend of luminosity calculation –Normalize using existing lumiCalc2.py The results are in CMS acceptance –No acceptance correction is applied –Leading muon pt > 30 GeV –2 nd leading muon pt > 20 GeV –Both muons |eta| < 2.1 lumiCalc2.py v1 lumiCalc2.py v3 lumiCalc2.py v2 3% 1.5% 7.5%

Slide 9 Double Muon Trigger Provided by Alexey Svyatkovskiy, Stoyan Stoynev, Hwidong Yoo

Slide 10 Samples & Event Selection Use DoubleMu PD –/DoubleMu/Run2011A-May10ReReco-v1/AOD ( ) –/DoubleMu/Run2011A-PromptReco-v4/AOD ( ) –/DoubleMu/Run2011A-05Aug2011-v1/AOD ( ) –/DoubleMu/Run2011A-PromptReco-v6/AOD ( ) –/DoubleMu/Run2011B-PromptReco-v1/AOD ( ) –Use HLT_DoubleMu6 + HLT_Mu13_Mu8 MC: DYM20 Fall11 42X Event selection: event selection using in DY differential cross section measurement –Baseline muon ID –Muon1 pt > 14 GeV, muon2 pt > 9 GeV –PF-based isolation

Slide 11 Efficiencies (1) Efficiency is determined using t&p method Measurement single muon object efficiency and event efficiency is estimated by eff (event) = eff(mu1) * eff(mu2) –This way doesn’t take into account for all system uncertainties and correlation between two legs of trigger. We should consider 2-3% level of systematic uncertainties on the determination. RunBv1:

Slide 12 Efficiencies (2) Efficiencies as function of run period and number of vertices

Slide 13 MC Inputs We use the MC inputs to determine absolute Z cross section

Slide 14 Luminosity Expectation (1) Predicted luminosity is determined using the number of Z candidates passing all selection, and efficiencies, acceptance with theoretical cross section (970 pb) from NNLO. Well agreement with lumiCalc2.py v2 ~0% 1.4% 1.8% 1.7% ~0% With v3 1.8% 3.3% 4.1% 4.5% 3.2%

Slide 15 Luminosity Expectation (2) Absolute Z cross section and luminosity prediction as a function of run period Merge 3 consecutive runs Merge 10 consecutive runs LumiCalc2.py v1 is used Used Run < T.S.

Slide 16 Luminosity Expectation (3) Test with lumiCalc2.py v2 Merge 3 consecutive runs Merge 10 consecutive runs Used Run <

Slide 17 Luminosity Expectation (4) Test with lumiCalc2.py v3 Merge 3 consecutive runs Merge 10 consecutive runs Used Run <

Slide 18 Z  ee Provided by Ilya Kravchenko, Jamila Butt, Youn Roh, Efe Yazgan Event yields after selection –Normalized integrated luminosity –No efficiency correction –No acceptance correction More corrections are needed to check the trend observed in Z  mumu Apply energy scale correction