Strategies for b-tagging performance using t t events at CMS

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1 Strategies for b-tagging performance using t t events at CMS Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Università di Pisa 6 th September 2011 Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 1 / 20

2 Process under study At LHC, Higgs bosons can be produced in association with b quarks Why investigating in this particular decay channel? High branching ratio for lower Higgs masses Of all SM processes, multijet production is the major source of background (bg). Accurately identified b-jets will help to reduce bg from hadronization of light quarks and gluons. In models beyond the SM, this final state may be even produced with larger cross section, such that a discovery is possible. Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 2 / 20

3 Entries Mean RMS Entries Mean RMS Generating events for simple analysis Using a Monte Carlo An event is the result of a single pp collision inside the CMS detector or a simulated collision event. Monte Carlo generators are used to simulate events. Events p T 300 Pythia generation of Z b b events Pythia generation of pp t t events Kinematical distributions that characterize this processes of b quark pt Events of b quark 250 eta p [GeV] T [rad] Figure: p T and η distribution for b quarks decayed from Z boson. Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 3 / 20

4 Invariant mass distribution MZ 2 = 2 E p 2 b quarks b quarks Selection of W µν µ Events W transverse mass Wtmass Entries Mean RMS Events Z mass Zmass Entries 4819 Mean RMS M [GeV] Events M [GeV] W mass Wmass Entries Mean RMS Figure: The invariant mass of the two b-quarks originated from the Z-boson (m Z = GeV) M [GeV] Figure: Reconstructed W transverse mass (up) and invariant mass (down). (m W = GeV) Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 4 / 20

5 B-tag efficiency Various b-tagging algorithms have been implemented in CMS efficiencies of these algorithms will have to be calibrated Calibration method: Top-quark based BR(t Wb) 100% t t events can be used to isolate jet samples with a highly enriched b-jet content. We will study the semimuonic t t decay with final state b bqq µν µ. where x (MC) b x (Data) tag = tag jets all jets = b jets all jets, ideal case: if x b 1 ε b f (ε 0 ) Trying to improve x b is our goal. Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 5 / 20

6 Preselection b-jet enriched sample from semimuonic t t events from Data and MC samples obtained requiring: to pass HLT IsoMu17 v5 (and v6, v7) at least one muon with: η < 2.1 and p T > 20 GeV; at least 4 jets with: η < 2.4 and p T > 30 GeV. N evt after preselection Data 2497 ± 50 MC 2456 ± 82 Table: Selected events from Data and MC are comparable. After this preselction the ratio between signal and background to the process S that we are looking for is: (t t semimu decay) = 1.72 ± 0.6. B Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 6 / 20

7 Comparison between Data and MC Figure: Control plot: kinematical distribution comparison between Data and MC samples after preselection of the events for E T. Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 7 / 20

8 Comparison between Data and MC Figure: Control plots: kinematical distributions comparison between Data and MC samples after preselection of the events for MET and E T 1. Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 8 / 20

9 Comparison between Data and MC Figure: Control plots: kinematical distributions comparison between Data and MC samples after preselection of the events for minm(jm,jn) k M(j and η k ) µ. Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 9 / 20

10 Comparison between Data and MC Figure: Control plots: kinematical distributions comparison between Data and MC samples after preselection of the events for (Φ MET, Φ µ) and η(j 3, j 4 ). Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 10 / 20

11 MVA performance What and Why TMVA: Toolkit for Multivariate Data Analysis with ROOT To improve the ratio S B S/B separation in absence of correlations between input variables Correlation Matrix (signal) mindijetmass detajet3jet4 detajet2jet3 himetlepton lepeta Linear correlation coefficients in % lepeta dphimetlepton detajet2jet3 detajet3jet mindijetmass How Likelihood ratio method (LHM) The likelihood ratio for event i is: y L(i) = L S(B) (i) = N var k L S (i) L S (i) + L B (i) ; p S(B),k (x k (i)) Correlation Matrix (background) mindijetmass detajet3jet4 detajet2jet3 himetlepton lepeta Linear correlation coefficients in % lepeta dphimetlepton detajet2jet3 detajet3jet mindijetmass Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 11 / 20

12 Figure: Likelihood distribution (y L ) for signal and background and choice of the optimal cut value. Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 12 / 20

13 Process N evt tot N sel after HLT, offline and MVA t t µ+ jets 4522 ± t t Non(µ+ jets) ± W µν µ+ jets ± W τν µ+ jets ± Z µµ+ jets ± Zbb 4517 ± Single t (s) 758 ± Single t (t) 2412 ± Single t (tw) 2302 ± Drell-Yan µµ ± Drell-Yan τ τ ± Data ± (N selected evt ) from MC Table: MVA selection results Improved ratio: S B 2.38 Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 13 / 20

14 Comparison between Data and MC after presel. and MVA selection Figure: Control plots: kinematical distributions comparison between Data and MC samples after preselection and MVA selection of the events for minm(jm,jn) k M(j k ) and η µ. Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 14 / 20

15 Comparison between Data and MC after presel. and MVA selection Figure: Control plots: kinematical distributions comparison between Data and MC samples after preselection and MVA selection of the events for (Φ MET, Φ µ) and η(j 3, j 4 ). Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 15 / 20

16 Summary of the summer work Getting practice with the CMS framework software first exercises and analysis; start with the main goal of this study: find a strategy to estimate b-tag efficiency; proceed with one of the known methods, the top quark method; preselection on Data/MC and S estimation; B use of the likelihood ratio based method to increase the purity of the selection to have b-enriched samples in the end. The next steps for this analysis will be the estimation of b-tagging efficiency; the systematic study and uncertainties; repeat this study for different methods and operation points. Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 16 / 20

17 Thanks for the attention!! Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 17 / 20

18 backup Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 18 / 20

19 in which the integrated luminosity from Data recorded by the HLT trigger is 324pb 1. Data: New summer 2011 data: /SingleMu/Run2011A PromptReco v4/aod for runs in the range [160404, ]with L=869pb 1. Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 19 / 20

20 Figure: Input distributions used to create PDF s of kinematical variables for signal and background. In order to improve efficiency of the method (likelihood ratio method) we have to find other kinematical variables. [Work in Progress] Stefania Vitillo Supervisor: Wolfgang Lohmann Tutor: Igor Marfin Strategies for b-tagging performance using t t events at CMS 20 / 20