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Pre-Approval

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  1. Authors: Pre-Approval Marcella Bona, Emanuele Di Marco, Joseph D. Lykken, Paolo Meridiani, Christopher Rogan, Chiara Rovelli, Ilaria Segoni, Maria Spiropulu, Thiago Tomei, Marco Zanetti ARC committee: Gautier Hamel de Monchenault (Chairperson), Jeffrey Berryhill, Cecilia Elena Gerber

  2. Z+jets candle analysis • Data-driven strategy to study properties of Z+jets production in final states with di-electrons and di-muons • Focus on LHC start-up - order of data at • Use two detector-wise orthogonal jets definitions: calo jets and track jets • Analysis goals: • (i) Investigate whether over yields ratio is constant as a function of • (ii) use track-jet counting, independently from calo-jet counting, to increase available signal statistics • (iii) Select a validated, pure Z+jets sample that can be used for detector commissioning at LHC start-up • (iv) Assuming (i) is positive, use jet multiplicity distribution as a probe for NP, typically responsible for an excess of events at large values of EWK-08-006 Pre-Approval, April 28

  3. Analysis Strategy Event Reconstruction and Cut-Based selection Maximum Likelihood Fit Background control sample Fit PDF validation Tests of the Fit sPlots Z boson “Candle” Over Yield ratio MET characterization/ corrections Probe of NP Background estimation EWK-08-006 Pre-Approval, April 28

  4. Analysis Strategy • single, non-isolated lepton triggers • high efficiency selection to allow maximal signal yield and background modelling • Jet clustering (calo/track) consistent with event primary vertex Event Reconstruction and Cut-Based selection Maximum Likelihood Fit Background control sample Fit PDF validation Tests of the Fit sPlots Z boson “Candle” Over Yield ratio MET characterization/ corrections Probe of NP Background estimation EWK-08-006 Pre-Approval, April 28

  5. Event Selection • Trigger selection • trigger menu • HLT single non-isolated muon and electron triggers with pT thresholds and L1 thresholds and for electrons and muons, respectively • Z boson reconstruction and primary vertex selection • Z candidates selected requiring • fraction of events with multiple Z candidates is found to be negligible - in this case Z with highest pT leptons chosen • Reconstructed primary vertex closest to Z candidate vertex in z (nominal beam axis, minimum ) is chosen as event primary vertex - in the absence of PU this corresponds to the highest reconstructed primary vertex ~100% • Choice of event primary vertex is essential for lepton selection and jet clustering EWK-08-006 Pre-Approval, April 28

  6. Electron selection • Electron identification • Use PixelMatchGsfElectrons • loose electron identification criteria (see table) • Vertex requirements • Consistency with event primary vertex, requiring: • Tracker isolation • consider tracks consistent with electron vertex in a cone of R < 0.4 with a veto cone of R > 0.015 • Require E/Gamma POG-developed ID EWK-08-006 Pre-Approval, April 28

  7. Muon selection • Muon identification • Use GlobalMuons • Vertex requirements • Consistency with event primary vertex, requiring: • Tracker isolation • consider tracks consistent with event primary vertex in a cone of R < 0.5 Require EWK-08-006 Pre-Approval, April 28

  8. Jet clustering • For Z + jets selection, everything is done as a function of inclusive jet multiplicity • We consider two type of jets (SISCone algorithm): • Calo-jets: jets clustered from the calorimeter (ECAL+HCAL) cells re-projected w.r.t. event primary vertex • Track-jets: jets clustered from tracks consistent with the event primary vertex • These two types of jets Requiring: Requiring: Have orthogonal detector systematics Probe different regions of phase space EWK-08-006 Pre-Approval, April 28

  9. Selection summary EWK-08-006 Pre-Approval, April 28

  10. Analysis Strategy • 1-D extended and un-binned maximum likelihood fit of used to determine signal and background yields • Independent fits done for each jet multiplicity • Data-driven strategy for determining line-shapes, testing and validating the fits Event Reconstruction and Cut-Based selection Maximum Likelihood Fit Background control sample Fit PDF validation Tests of the Fit sPlots Z boson “Candle” Over Yield ratio MET characterization/ corrections Probe of NP Background estimation EWK-08-006 Pre-Approval, April 28

  11. Maximum Likelihood Fit • One-dimensional unbinned and extended maximum likelihood fit based on • ML fits to Monte Carlo signal samples indicated that the fit parameters do not depend on the jet multiplicity within precision of target luminosity • Signal PDF held fixed (assumed from inclusive Z study) - assumption can be later validated • 2 species yields + 2 bkg shape parameters = 4 parameter fit total number of events entering the fit (i.e. extended likelihood) Ni=signal and backgrounds yields Z+jets: 1dim fit: P=PDF(mll) EWK-08-006 Pre-Approval, April 28

  12. Maximum Likelihood Fit • Non-Gaussian effects in signal lepton invariant mass distribution result from • mismatched signal events, where one muon is from the Z and the other coming from jets, muons from • one of the two leptons irradiating a photon, shifting the measured Z mass lower • Use Crujiff function to parameterize signal: EWK-08-006 Pre-Approval, April 28

  13. Analysis Strategy • Background predominantly QCD • Use control sample from data to measure line-shape for fit Event Reconstruction and Cut-Based selection Maximum Likelihood Fit Background control sample Fit PDF validation Tests of the Fit sPlots Z boson “Candle” Over Yield ratio MET characterization/ corrections Probe of NP Background estimation EWK-08-006 Pre-Approval, April 28

  14. QCD background control sample • Background in both electron and muon final states is dominated by QCD contribution • Shape of background PDF’s is studied using “anti-lepton” sample, obtained by inverting tracker-isolation cut: • Other backgrounds accounted for by floating shape parameters (distribution well-described by second-order polynomial) EWK-08-006 Pre-Approval, April 28

  15. Analysis Strategy • Toy Monte Carlo experiments used the expected statistical error on the signal yields • Check for fit biases • Test confidence interval coverage Event Reconstruction and Cut-Based selection Maximum Likelihood Fit Background control sample Fit PDF validation Tests of the Fit sPlots Z boson “Candle” Over Yield ratio MET characterization/ corrections Probe of NP Background estimation EWK-08-006 Pre-Approval, April 28

  16. Tests of the Fit • Toy Monte Carlo experiments indicate and expected error on signal event yields of 2% for (16% for ) • Similar errors for • Larger statistics with track-jet counting improves precision by at least a factor of 2 • Toy MC experiments also prove ML fit is unbiased and that 68% confidence interval comuted using likelihood ratio correctly covers true number of events • See extra slides for details EWK-08-006 Pre-Approval, April 28

  17. Fit Results EWK-08-006 Pre-Approval, April 28

  18. Fit Results EWK-08-006 Pre-Approval, April 28

  19. Fit Results EWK-08-006 Pre-Approval, April 28

  20. Fit Results EWK-08-006 Pre-Approval, April 28

  21. Analysis Strategy Event Reconstruction and Cut-Based selection • Test a posteriori the validity of assumed PDF’s from ML fit using sPlots technique Maximum Likelihood Fit Background control sample Fit PDF validation Tests of the Fit sPlots Z boson “Candle” Over Yield ratio MET characterization/ corrections Probe of NP Background estimation EWK-08-006 Pre-Approval, April 28

  22. sPlots ML Fit Validation • The ML fit is extended to an additional variable that is uncorrelated with the dilepton invariant mass and also discriminates between signal and background • We use the variable , which satisfies these requirements (see backup slide) • Allows for a posteriori validation of Z line-shape PDF using sPlots EWK-08-006 Pre-Approval, April 28

  23. sPlots ML Fit Validation • 1D ML fit performed with each variable to check other • the output of the fit is used to compute sWeights • the sWeights-weighted plot of the variable taken out of the fit produces the distribution for signal events. Each event contributes to the plot proportionally to its probability of being signal • By comparing the distribution with PDF obtained in the nominal fit we can use the other variable(s) in the fit to test our assumption on the functional form of the PDF EWK-08-006 Pre-Approval, April 28

  24. sPlots ML Fit Validation NOTE: these are NOT fits EWK-08-006 Pre-Approval, April 28

  25. Analysis Strategy Event Reconstruction and Cut-Based selection • Using tested and validated ML fit we can study event yields as a function of inclusive jet multiplicity Maximum Likelihood Fit Background control sample Fit PDF validation Tests of the Fit sPlots Z boson “Candle” Over Yield ratio MET characterization/ corrections Probe of NP Background estimation EWK-08-006 Pre-Approval, April 28

  26. over yields ratio From: From: CMS AN2008-091 S.D. Ellis et al., “W’s, Z’s AND JETS”, Phys. Lett. 154B (1985) 435 Alpgen (CSA07) generator level study of Z+jets events Berends et al.: EWK-08-006 Pre-Approval, April 28

  27. over yields ratio EWK-08-006 Pre-Approval, April 28

  28. over yields ratio With candle selection: EWK-08-006 Pre-Approval, April 28

  29. over yields ratio Fit probabilities between 75% and 94% EWK-08-006 Pre-Approval, April 28

  30. Analysis Strategy • sPlots framework also allows for statistical background subtraction without a decrease in signal statistics • Can create ‘pure’ Z(ll)+jets dataset to use for various applications Event Reconstruction and Cut-Based selection Maximum Likelihood Fit Background control sample Fit PDF validation Tests of the Fit sPlots Z boson “Candle” Over Yield ratio MET characterization/ corrections Probe of NP Background estimation EWK-08-006 Pre-Approval, April 28

  31. sPlots background subtraction • An interesting feature of the sPlots is the fact that the integral of the weighted plot of a signal distribution is equal to the number of signal events found in the ML fit • This means that the sPlots can be used to perform the subtraction of the background component without losing statistics on signal • Z+jets dataset weighted with computed sWeights from nominal 1-D fit corresponds to a pure, validated signal sample EWK-08-006 Pre-Approval, April 28

  32. MET characterization with sPlots • Can use pure Z()+jets dataset to study calorimetric detector response and MET measurement • With only MIP calorimetric deposits, Z() pT can be used as ‘generator level’ MET • Allows for characterization of detector response and validation/tuning of full simulation calorimetry EWK-08-006 Pre-Approval, April 28

  33. MET correction with sPlots • Using similarities between W/Z + jets topologies, we can use events to calibrate MET for with event-by-event correction • Take a ‘W-like’ view of Z+jets events, treating one muon leg as an unobserved neutrino • Decompose MET into two orthogonal components: • Calibrate each component separately EWK-08-006 Pre-Approval, April 28

  34. NP probe with Z candle We can observe new physics in beyond the standard model scenarios that yield an excess of Z’s Assuming to be sufficiently constant, we can use the lower jet multiplicities to predict the rates in the higher multiplicities and measure any deviations • We consider LM4 as a benchmark scenario where Z’s are produced in the decays of neutralinos • We perform a set of Toy MC experiments where the SM signal and background are generated in addition to LM4 events EWK-08-006 Pre-Approval, April 28

  35. NP probe with Z candle EWK-08-006 Pre-Approval, April 28

  36. background estimation for NP searches EWK-08-006 Pre-Approval, April 28

  37. Conclusions • We present a data-driven strategy to study properties of Z+jets production in final states with di-electrons and di-muons that, with at achieves the following goals: • (i) Investigate whether over yields ratio is constant as a function of • (ii) use track-jet counting, independently from calo-jet counting, to increase available signal statistics • (iii) Select a validated, pure Z+jets sample that can be used for detector commissioning at LHC start-up • (iv) Assuming (i) is positive, use jet multiplicity distribution as a probe for NP, typically responsible for an excess of events at large values of EWK-08-006 Pre-Approval, April 28

  38. We would like to add the MET characterization example (one paragraph) from the jet-dimuon Pt balance study with the sPlot subtraction so Figure 9 would like this: EWK-08-006 Pre-Approval, April 28

  39. EXTRA SLIDES EWK-08-006 Pre-Approval, April 28

  40. Datasets Fall08 samples Summer08 samples CMSSW_2_1_X Full Simulation “Ideal” conditions EWK-08-006 Pre-Approval, April 28

  41. MET correction with sPlots Can use candle sWeighted sample to calibrate MET • Consider an orthogonal decomposition of MET • Use Z candle to measure same components with Z, using Z candidate and leading (2nd leading) muon • Derive correction factors • bin correction factors in and / , fit distributions to get • Correct events EWK-08-006 Pre-Approval, April 28 such that:

  42. MET correction with sPlots EWK-08-006 Pre-Approval, April 28

  43. MET correction with sPlots After Z+jets candle derived corrections, the characteristic Jacobian edge in the W MT distribution is recovered EWK-08-006 Pre-Approval, April 28

  44. “MHT” Variable EWK-08-006 Pre-Approval, April 28

  45. references for 21X analyses: • CMS PAS • EWK-08-006 • https://hypernews.cern.ch/HyperNews/CMS/get/EWK-08-006.html • CMS notes • W(l)+jets/Z(ll)+jets Ratio Analysis: CMS AN-2009/045 • Z(ll)+jets Candle Analysis: CMS AN-2009/xxx • previous EWK talks on 21X analysis: • PAS candle status: M.Bona, Mar 3rd 2009 • analysis updates: M.Pierini, Feb 6th 2009, M.Bona and I.Segoni, Jan 23rd 2009 C.Rogan, Jan 20th 2009 • references for 16X analyses: • CMS notes • Z+jets & W+jets Alpgen Validation: CMS AN-2008/091 • Z(ll)+jets Candle Analysis: CMS AN-2008/092, CMS AN-2008/095 • W(l)/Z(ll)+jets Ratio Analysis: CMS AN-2008/096, CMS AN-2008/105 • previous EWK talks on 16X analysis: • summary by M.Pierini Nov 11th 2008 • note release: E. Di Marco, C. Rogan, and I. Segoni: Oct 17th 2008

  46. Vector-boson + jets: strategy • general strategy • single non isolated leptonic trigger • Z mass window: [60, 110] GeV/c2 • lepton pt cut: • first leg -> higher than the trigger threshold: pt > 20 GeV/c • looser second leg: pt > 10 GeV/c • lepton selection: optimized for the W+jets ratio analysis and then loosen it to allow the highest efficiency for the standard candle • jet clustering (SisCone with 0.5)‏ • calorimeter jets: E > 30 GeV and || < 3 • track jet: pt > 15 GeV/c and || < 2.4 • 1D maximum likelihood fit: • Z mass with two species: signal+background • fixing the signal shape parameters from the inclusive study and floating the background parameters • 2D fit and “sPlots” to verify shapes on data: both for signal and background

  47. Vector-boson + jets: lepton selection • lepton selection: • global muons or optimized electron ID • analysis specific selection: • vertex variables: xy z(-PV)‏ • isolation variables: i piT/pT, ETECAL ETHCAL • optimized simultaneously for the W+Njets final states where the background rejection is more critical • for the candle we want the highest efficiency possible • maintaining reliability on the sample for example: being able to calculate efficiencies if needed • establishing a fit procedure that is robust against background, taking advantage of the abundance of the Z signal and the separation power of the Z mass Emanuele's talk, Jan 16th 2009 EWK electron meeting • xy <0.04 cmz(-PV) <0.12 cm • i piT/pT<0.15 • xy <0.02 cmz(-PV) <0.15 cm • i piT/pT<0.30 Z() candle Z(ee) candle

  48. Vector-boson + jets: electron final state Z(ee) candle signal selection efficiencies Z(ee) candle final sample composition calojets trackjets

  49. Vector-boson + jets: muon final state Z() candle signal selection efficiencies Z() candle final sample composition

  50. Vector-boson + jets: muon event sample Z(mm) candle final sample composition calojets trackjets