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2016 dat 2016 dataset aset Huajie Cheng 2020.11.13 Introduction - PowerPoint PPT Presentation

Tau Energy Scale in Tau Energy Scale in 2016 dat 2016 dataset aset Huajie Cheng 2020.11.13 Introduction TES measurement using TauFW Preliminary results using 2016 dataset, in mu-tau events Coarse ES variations applied to real


  1. Tau Energy Scale in Tau Energy Scale in 2016 dat 2016 dataset aset Huajie Cheng 2020.11.13

  2. Introduction ◆ TES measurement using TauFW ◼ Preliminary results using 2016 dataset, in mu-tau events ◼ Coarse ES variations applied to real tau • -3% → 3% in steps of 1% (0.2% for final measurement) ◼ Compare the impacts in DY and TT processes ◼ Fit independently per DM • Only in DY process • Use m vis and m τ as observables • Fix “r” parameter and scan likelihood profile in TES • Fit the NLL profile by asymmetric parabola function 2

  3. Mu-tau selections ◆ Baseline ➢ Single muon trigger & SF applied ➢ Muon • pT > 23 GeV, |eta| < 2.1 or 2.4 • |dxy| < 0.045, |dz| < 0.2 • medium ID, rel. iso < 0.15 ➢ Tau • pT > 20 GeV, |eta| < 2.3, |dz|<0.2, decay modes 0, 1, 10, 11 • DeepTau2017v2p1VSjet Tight WP • DeepTau2017v2p1VSmu Tight WP • DeepTau2017v2p1VSe VVLoose WP ➢ Opposite sign mu- tau pair with ΔR > 0.5 and highest mu and tau pT ➢ Extra-lepton veto ◆ m T < 50 GeV 3

  4. TES systematic uncertainties ◆ All the shape uncertainties are not yet in place Nuisance Parameter Distribution Uncertainty Applied to ± 2.5% lumi lnN all, except QCD ± 2% mu Eff lnN all, except QCD tau ID shape from recommendation ZTT, TTT ± 2% DY cross section lnN DY ± 6% ttbar cross section lnN ttbar ± 5% signle top cross section lnN single top ± 5% VV cross section lnN diboson ± 8% W+jets normalization lnN W ± 10% QCD normalization lnN QCD ± 15% jet -> 𝜐 fake rate lnN ZJ, W, QCD, TTJ, ST ± 5% on jet → 𝜐 energy jet -> 𝜐 fake ES shape ZJ, W, TTJ ℓ -> 𝜐 fake rate shape from recommendation ZL, TTL ± 2% on ℓ → 𝜐 energy ℓ -> 𝜐 fake ES shape ZL, TTL apply weight ± 10% Z pt reweight shape DY 4

  5. Datacard for DM0 ◆ Not all syst included, e.g. tid 5

  6. Likelihood profile scan ◆ Profile the NLL as a function of tes ◆ And the fit with asym parabola function DM1 DM10 DM11 DM0 DM1 DM10 DM11 6

  7. Fit result ◆ DM1 for m_tau DM1 7

  8. Fit result ◆ DM10 for m_tau DM10 8

  9. Fit result ◆ DM11 for m_tau DM11 9

  10. Fit result ◆ DM0 for m_vis DM0 10

  11. Fit result ◆ DM1 for m_vis DM1 11

  12. Fit result ◆ DM10 for m_vis DM10 12

  13. Fit result ◆ DM11 for m_vis DM11 13

  14. Results ◆ Finally got the TES values w.r.t different DMs Before fit After fit Ref. ◆ Quite ugly due to very coarse variation step and missing uncertainties ◆ Jobs with step=0.2% almost done! ◆ Asked to give a status report next Monday 14

  15. Some distributions -3% TES +3% TES ◆ Here are the plots with all DMs combined, the fit will be performed with each DM separately 16

  16. Some distributions Negligible impact from ttbar ◆ Will only apply the TES in ZTT for the measurement 17

  17. Framework: from nanoAOD to final plots 18

  18. Sample list 19

  19. Air condition monitoring in the clean room ◆ Took 4 samples in each rooms (2 for room 110) 10000 0.3um 0.5um 1.0um 2.0um 5.0um 10.0um Counts in 5 mins (14.16L) 1000 100 10 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Cases

  20. IV-curve ◆ Finally managed to perform IV measurement remotely by LabView ➢ Change from GPIB to RS232-to-USB cable

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