Multivariate Analysis for Z eμ

1. Multivariate Analysisfor Zeμ Kyoko Yamamoto Iowa State University 8TeV eμ/eτ/μτ LFV resonance search meeting January 22, 2013

2. MC12 Zeμ Sample Request • Job option file: MC12.180333.Pythia8_AU2MSTW2008LO_Zemu_LeptonFilter.py • Generator: Pythia 8.165 • Filter efficiency: 0.62291 • Lepton filter: pT(l) > 5GeV, |η(l)|< 2.8, and N(l) = 2 • Number of events: 50K (10 files) • Full simulation • Zeμ validation wiki page including validation plots: https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/LFVinZemu • Savannah request URL: https://savannah.cern.ch/task/?38680 • Request has been approved, and simulation will berunning soon • Using my private full simulated Zeμ sample (50K events) without the lepton filter until the official sample is available Kyoko Yamamoto

3. Event Selection • Trigger (unprescaled for all periods) • Electron: EF_e24vhi_medium1 || EF_e60_medium1 • Good Run Lists for data only • Vertex: primary vertices associated with more than 3 tracks • Data quality: veto LAr noise burst, Tile corrupted, or incomplete events • Jet cleaning (cleaning should be applied to AntiKt4TopoEMjets) • Use only jets that survive the lepton overlap removal (remove jets with ΔR < 0.3 to SELECTED electrons/muons) • Remove events with at least one LOOSE bad or ugly jet with pT(calibrated jet) ≧ 20GeV for both data and MC (jet_isBadLooseMinus, jet_isUgly) • Hot tile calorimeter cleaning for data only • FCal cleaning for both data and MC • Exactly one high pT isolated tight e and one high pTisolated combined μ • Veto events with 2nd high pT “no isolation required” loose electron or combined muon • Trigger matching • Opposite charge of electron-muonpair • Invariant mass: 66 < Meμ< 116 GeV Not applied |Δφ(e,μ)| > 2.7 at this time Kyoko Yamamoto

4. Dilepton Mass Distribution Opposite charge pair Same charge pair • Signal (Zeμ) corresponding to the LEP1 BR limit (BR <1.7×10-6) is overlaid with the stuck background histogram • Our MVA analysis does not include QCD background, but QCD is small • NOS(QCD)~ NSS(QCD) • W+jetsample shows statistical fluctuations Kyoko Yamamoto

5. MET and Jet Distributions in 86 < M(eμ) < 96 GeV (Z-mass Pole) Missing transverse energy Transverse momentum for leading jet More plots: http://hep-int1.physics.iastate.edu/~kyoko/IowaState/Zemu/ Kyoko Yamamoto

6. Multivariate Analysis (MVA) • TMVA (Toolkit for MVA) • ROOT-integrated machine learning environment for the processing and parallel evaluation of multivariate classification and regression techniques • Including many multivariate methods • Boosted Decision Trees (BDT) • Sequential application of cuts splits the data into nodes, where the final nodes classify an event as signal or background Ref: HelgeVoss, “Decision Trees and Boosting,” TMVA Workshop , CERN, 21 Jan 2011 Kyoko Yamamoto

7. Variables for MVA • Picking up some variables to test MVA although some variables are correlated • Missing transverse energy • Using MET_RefFinal_STVF after the MET correction • Leading jet pT(if no jets, pT(1st jet) = 0) • Using AntiKt4LCTopojets after the jet calibration • AntiKt4LCTopo improves the resolution and is recommended by the Jet/EtMissgroup and Arantxa (SM Jet convener) • Number of jets for pT(jet) > 20GeV and |y| < 4.5 • Azimuth angle between electron and muon:Δφ(e,μ) • DileptonpT(eμ) • pT(e) (after pT(e) > 25GeV cut) • pT(μ) (after pT(μ) > 25GeV cut) Kyoko Yamamoto

8. Correlation Matrix Background Data data • Some variables are highly correlated as we expected • pT(jet) and N(jet), Δφ(e,μ) and pT(eμ) Kyoko Yamamoto

9. MVA Response: BDT Tested signal and background Tested background and applied the BDT result to data Background shape for BDT response agrees with data well Kyoko Yamamoto

10. Signal and Background Efficiencies High background rejection at high signal efficiency  It looks good! Kyoko Yamamoto