Report Jet-veto SF and Uncertainties Jet smearing

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Presentation transcript:

Report Jet-veto SF and Uncertainties Jet smearing Lailin Xu, Haijun Yang, Bing Zhou The University of Michigan

Outline Jet-veto SF and uncertainties Effects of Jet smearing on MET MET with/without METUtility WW/top/tautau background uncertainties 26.05.2018

Jet Veto SF and Uncertainties SF =eZZ(data)/ eZZ(MC) = eZ(data)/eZ(MC) Control samples: Data (p833, 4.7 fb-1) Z ee and Z mm MC (MC11C): Z ll MC@NLO/Herwig, ALPGEN, PYTHIA Systematic uncertainties Parton showing modeling (with diff. MC generator) JES/JER (using ATLAS package to smear jet ET)

Jet-veto SF Results SF = 0.96 (both for ee and for mm) from MC@LNO Z MC  compared to WW analysis: SF = 0.963 Systematic uncertainty < 0.3% due to JES/JER ~ 5% due to different MC generator (Alpgen gives SF = 1.00; Pythia gives SF = 1.02)

Jet smearing effects on MET:ee With jet smearing (for all the EM-jets by hand) Compared to no jet smearing

Jet smearing effects on MET:mm With jet smearing (for all the EM-jets by hand) Compared to no jet smearing

MET with METUtility:ee MET with the Tool Compared to without tool

MET with METUtility:mm MET with the Tool MET without the Tool

WW/top/tautau background estimation method formular: are efficiency factor for ee and mm events Here Z+jets is the DY contribution without Ztautau

Cut flow for ee (final 34 events)

Cutflow:mm (final 45 events)

Cut flow em (final numner 15)

estimated background after all cuts data estimated background mc background (WW/ttbar/tW/tautau) ee 6.4+/-1.8+/-0.020 4.7+/-0.4+/-0.69 mm 8.4+/-2.4+/-0.027 10.8+/-1.2+/-1.66

Error propagation in data driven method

back up

Compared to no jet smearing

With jet smearing for all EM jets (by hand)

Compared to no jet energy smearing