VBF H inv L1 Prefire Impact S. Narayanan , on behalf of the VBF H - PowerPoint PPT Presentation

VBF H → inv L1 Prefire Impact S. Narayanan , on behalf of the VBF H → inv group Higgs PAG, 30/07/2018 S. Narayanan (MIT) VBF H → inv 30/07/2018 1 / 17

Overview of problem -1 36 fb (13 TeV) ◮ L1 paths may prefire due to mis-timed Events 9000 Data Data CMS W+jets [QCD] W+jets [QCD] ECAL trigger primitives (TPs) in 8000 W+jets [EWK] W+jets [EWK] Top Top endcap Z+jets [EWK] Z+jets [EWK] 7000 Z+jets [QCD] Z+jets [QCD] Diboson Diboson ◮ Prefire ⇒ next event (interesting 6000 QCD QCD 5000 physics) cannot fire L1A 4000 ◮ Reported weeks ago, heavily discussed 3000 in L1, PPD, JME, etc meetings ab 2000 ◮ What is the impact on this analysis? 1000 ◮ High p T and forward jets can set off 0 Data/Pred. − − − − − 5 4 3 2 1 0 1 2 3 4 5 1.5 forward ECAL L1! ◮ Mis-measured jet energy can bias p miss 1 T a https://indico.cern.ch/event/737391/ 0.5 − − − − − 5 4 3 2 1 0 1 2 3 4 5 b https://indico.cern.ch/event/729127/ η Jet 1 S. Narayanan (MIT) VBF H → inv 30/07/2018 2 / 17

Determining prefire inefficiency 1. Select “un-prefirable” events ◮ Exploit trigger rules by looking for L1A at bx0 and bx-3 ◮ N. Smith has kindly provided a filter to select such events 1 2. Compute rate of L1IsoEG30 TP prefire in this set of unbiased events ◮ Start with this L1 path since it is unprescaled ◮ Most plots will use the following definition of efficiency: ǫ IsoEG30 bx-1 = N (jets matched to L1IsoEG30 in bx-1) N (all jets) ◮ This is the easiest observable to translate into analysis 1 https://github.com/nsmith-/PrefireAnalysis/ S. Narayanan (MIT) VBF H → inv 30/07/2018 3 / 17

Frequency of L1IsoEG30 prefire in JetHT ◮ Averaging over 2016 × 3 10 5 1 5 24 | L1IsoEG BX=-1 eff η Number of BX-1 jets η | ◮ Events pass p miss filters CMS CMS CMS CMS CMS CMS CMS 22 4.5 0.9 4 T 20 JetHT JetHT 4 0.8 3 ◮ Central jets pass ID 18 3.5 0.7 2 16 ◮ Only ∼ 0 . 2% of data is 3 0.6 1 14 un-prefirable 2.5 0.5 0 12 − 10 ◮ Not enough data to 2 0.4 1 8 − 1.5 0.3 2 focus on VBF-like 6 − 1 0.2 3 event topologies 4 − 0.5 0.1 4 2 ◮ However, effect should − 0 0 5 0 − − − 100 200 300 400 500 600 3 2 1 0 1 2 3 factorize to jets φ p [GeV] T ◮ Hot tower much more Efficiency Number of jets likely to preire S. Narayanan (MIT) VBF H → inv 30/07/2018 4 / 17

Removing the spike ◮ Turn-on modified slightly with spike removed ◮ Reject any jet within 0 . 2 of ( − 2 . 81 , 2 . 07) ◮ Will remove events containing such a jet in the analysis 5 1 5 | L1IsoEG BX=-1 eff η Number of BX-1 jets η | CMS CMS CMS CMS CMS CMS CMS 1400 4.5 0.9 4 JetHT JetHT 4 0.8 3 1200 φ η φ η | -2.01|>0.4 or | +2.81|>0.4 | -2.01|>0.4 or | +2.81|>0.4 3.5 0.7 2 1000 3 0.6 1 800 2.5 0.5 0 − 2 0.4 1 600 − 1.5 0.3 2 400 − 1 0.2 3 200 − 0.5 0.1 4 − 0 0 5 0 − − − 100 200 300 400 500 600 3 2 1 0 1 2 3 φ p [GeV] T S. Narayanan (MIT) VBF H → inv 30/07/2018 5 / 17

PD dependence ◮ Looking in slice 2 . 75 < | η | < 3 ◮ Both MET and SingleMuon have a weaker inefficiency than JetHT at low p T ◮ Caused by correlations between event topology, selected triggers, and L1 seeds IsoEG30 BX=-1 eff IsoEG30 BX=-1 eff 1.4 CMS 1.4 CMS CMS MET MET SingleMuon SingleMuon 1.2 1.2 JetHT JetHT 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 100 200 300 400 500 600 0 100 200 300 400 500 600 p [GeV] EM p [GeV] T T S. Narayanan (MIT) VBF H → inv 30/07/2018 6 / 17

PD dependence in MET PD ◮ Prefire decreases p miss in events with real p miss T T ◮ ECAL pulse attributed to bx-1 can zero-suppress part of jet in bx0 ◮ Both online and offline p miss can be biased T ν ν ν ν p mi s s T p mi s s T Z Z = ⇒ E C A L D e p o s i t H C A L H C A L D e p o s i t s D e p o s i t s S. Narayanan (MIT) VBF H → inv 30/07/2018 7 / 17

PD dependence in JetHT PD ◮ Prefire decreases p T and H T in multijet events (most of JetHT) ◮ ECAL pulse attributed to bx-1 can zero-suppress part of jet in bx0 ◮ Both online and offline jet p T s can be biased p m i s s = ⇒ T E C A L D e p o s i t H C A L H C A L D e p o s i t s D e p o s i t s S. Narayanan (MIT) VBF H → inv 30/07/2018 8 / 17

SingleMuon and FinOR ◮ A mis-timed ECAL TP lowers probability that MET/MHT L1/HLT will fire on real- p miss events T ◮ These jets can have weird energy fractions ⇒ cleaned from MHT ◮ Even offline p miss can be biased due to zero suppression T ◮ Same is true for any trigger that uses ECAL ◮ SingleMuon triggers should be largely unbiased ◮ Compare to global trigger “Final OR” decision ◮ Efficiency definition changes slightly because FinOR cannot be matched to specific objects ǫ FinOR bx-1 = N (events w/ exactly 1 endcap jet and FinOR bx-1) N (events w/ exactly 1 endcap jet ) S. Narayanan (MIT) VBF H → inv 30/07/2018 9 / 17

L1EG30Iso vs L1EG40 vs GT FinOR ◮ Non-isolated TTs can show up in L1EG40 (not prescaled) ◮ In addition, ECAL deposits can prefire other triggers, so compare to global trigger ◮ Inefficiency increases slightly, but generally shows the same PD dependence ◮ Larger ǫ because more triggers to prefire IsoEG30 BX=-1 eff IsoEG30,EG40 BX=-1 eff FinOR BX=-1 eff 1.4 CMS 1.4 CMS 1.4 CMS MET MET MET SingleMuon SingleMuon SingleMuon 1.2 1.2 1.2 JetHT JetHT JetHT 1 1 1 0.8 0.8 0.8 0.6 0.6 0.6 0.4 0.4 0.4 0.2 0.2 0.2 0 0 0 0 100 200 300 400 500 600 0 100 200 300 400 500 600 0 100 200 300 400 500 600 p [GeV] p [GeV] p [GeV] T T T S. Narayanan (MIT) VBF H → inv 30/07/2018 10 / 17

Final correction ◮ SingleMuon measurement has large stat uncertainties above 300 GeV ◮ Use SingleMuon below 300 GeV and JetHT above 300 GeV ◮ Apply correction from FinOR inefficiency ◮ Should capture all effects ◮ Lose up to 20% of signal in tails -1 -1 fb (13 TeV) Events/400 GeV 5 1 | L1IsoEG BX=-1 eff CMS η | 500 CMS CMS CMS CMS CMS CMS × × ε ε VBF H(inv) VBF H(inv) JetHT JetHT 4.5 0.9 L1 L1 SingleMuon 4 0.8 400 φ η VBF H(inv) VBF H(inv) | -2.01|>0.4 or | +2.81|>0.4 3.5 0.7 300 3 0.6 2.5 0.5 � (1 − ǫ ( p j 200 T , η j )) SF = 2 0.4 100 j ∈ jets 1.5 0.3 0 1 0.2 Data/Pred. 0 500 1000 1500 2000 2500 3000 3500 4000 1.2 0.5 0.1 1 0 0 0.8 100 200 300 400 500 600 p [GeV] 0 500 1000 1500 2000 2500 3000 3500 4000 T m [GeV] jj S. Narayanan (MIT) VBF H → inv 30/07/2018 11 / 17

SM backgrounds ◮ We have always seen the dip at 2.5-3 in leading jet η ◮ In principle, this is correlated between all regions -1 -1 -1 × 3 10 36 fb (13 TeV) 36 fb (13 TeV) 36 fb (13 TeV) 700 Events Events Events 9000 CMS Data Data CMS Data Data CMS Data Data VBF H(inv) VBF H(inv) W+jets [QCD] W+jets [QCD] Z+jets [QCD] Z+jets [QCD] Z+jets [QCD] Z+jets [QCD] 8000 600 10 W+jets [EWK] W+jets [EWK] Z+jets [EWK] Z+jets [EWK] W+jets [QCD] W+jets [QCD] Top Top Top Top Z+jets [EWK] Z+jets [EWK] 7000 Z+jets [EWK] Z+jets [EWK] W+jets [EWK] W+jets [EWK] W+jets [EWK] W+jets [EWK] Z+jets [QCD] Z+jets [QCD] 500 W+jets [QCD] W+jets [QCD] Top Top 8 Diboson Diboson Diboson Diboson Diboson Diboson 6000 QCD QCD QCD QCD QCD QCD 400 5000 6 4000 300 4 3000 200 2000 2 100 1000 0 0 0 Data/Pred. − − − − − Data/Pred. − − − − − Data/Pred. − − − − − 5 4 3 2 1 0 1 2 3 4 5 5 4 3 2 1 0 1 2 3 4 5 5 4 3 2 1 0 1 2 3 4 5 1.5 1.5 1.5 1 1 1 0.5 0.5 0.5 − − − − − − − − − − − − − − − 5 4 3 2 1 0 1 2 3 4 5 5 4 3 2 1 0 1 2 3 4 5 5 4 3 2 1 0 1 2 3 4 5 η η η Jet 1 Jet 1 Jet 1 S. Narayanan (MIT) VBF H → inv 30/07/2018 12 / 17

Correcting SM backgrounds (FinOR) ◮ Region between 2.5-3 closes better after correction × 3 -1 -1 -1 10 36 fb (13 TeV) 36 fb (13 TeV) 36 fb (13 TeV) Events Events Events 9000 CMS Data Data CMS Data Data CMS Data Data VBF H(inv) VBF H(inv) W+jets [QCD] W+jets [QCD] Z+jets [QCD] Z+jets [QCD] 600 Z+jets [QCD] Z+jets [QCD] 8000 10 W+jets [EWK] W+jets [EWK] Z+jets [EWK] Z+jets [EWK] W+jets [QCD] W+jets [QCD] Top Top Top Top Z+jets [EWK] Z+jets [EWK] 7000 Z+jets [EWK] Z+jets [EWK] W+jets [EWK] W+jets [EWK] W+jets [EWK] W+jets [EWK] 500 Z+jets [QCD] Z+jets [QCD] W+jets [QCD] W+jets [QCD] Top Top 8 Diboson Diboson Diboson Diboson Diboson Diboson 6000 QCD QCD QCD QCD QCD QCD 400 5000 6 4000 300 4 3000 200 2000 2 100 1000 0 0 0 Data/Pred. − − − − − Data/Pred. − − − − − Data/Pred. − − − − − 1.5 5 4 3 2 1 0 1 2 3 4 5 1.5 5 4 3 2 1 0 1 2 3 4 5 1.5 5 4 3 2 1 0 1 2 3 4 5 1 1 1 0.5 0.5 0.5 − − − − − − − − − − − − − − − 5 4 3 2 1 0 1 2 3 4 5 5 4 3 2 1 0 1 2 3 4 5 5 4 3 2 1 0 1 2 3 4 5 η η η Jet 1 Jet 1 Jet 1 S. Narayanan (MIT) VBF H → inv 30/07/2018 13 / 17