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A first look at W -> e n + jets events

A first look at W -> e n + jets events. By Alessandro Tricoli In collaboration with Monika Wielers Rutherford-Appleton Laboratory. RAL ATLAS Physics Meeting 19 th December 2006. Overview. Long term goal: W + jets cross section as function of jet multiplicity Immediate goal:

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A first look at W -> e n + jets events

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  1. A first look at W -> en + jets events By Alessandro Tricoli In collaboration with Monika Wielers Rutherford-Appleton Laboratory RAL ATLAS Physics Meeting 19th December 2006

  2. Overview • Long term goal: • W + jets cross section as function of jet multiplicity • Immediate goal: • set up analysis tools for CSC Note W/Z +jets • check ATLAS electron and jet reconstruction performances • estimate systematic uncertainties • The only W->en +jets MC samples available now are SUSY samples (with very high filter cuts) but physics convenors promised a production dedicated to SM studies. • In this presentation • I will describe W->en +jets event selection algorithm • show basic distributions, main focus on jets and jet algorithms: • electron Et, Eta • jet Et, jet multiplicity with different jet algorithms • at Detector and Generator levels • Conclusions and Outlook Alessandro Tricoli, RAL

  3. Old and new multi-jet MC’s The large energies and luminosity at the LHC make final states with several hard and well separated jets a rather common phenomenon, (i.e. from hard QCD radiative processes, from decays of W, Z, top, Higgs, SUSY particles, etc.) SM multi-jet production processes , i.e W/Z + jets, important background to new physics searches. • Classical MC generators such as PYTHIA and HERWIG: • parton-level M.E. calculations atLO then combine it • with the partonic evolution given by the parton-shower (LL approx.). • Other recent MC, such as MC@NLO: • hard process calculations to NLO then combine it with the LL-parton shower evolution. • Problem of matching between hard process and parton shower. In the case of large jet multiplicities, the complexity of the matrix element evaluation and of its singularity structure prevents the application of these approaches Alessandro Tricoli, RAL

  4. Alpgen Multi-parton hard processes MC’s in hadronic collisions: • New strategies has been introduced for mergingthe exact matrix • elements at the leading order in QCD and EWwith the parton shower. • CKKW method (applied on SHERPA MC): This involves a re-weighting of the matrix element weights with Sudakov form factors (non-emission prob.), and the veto of shower emissions in regions of phase-space already covered by the parton-level configuration • ALPGEN: Requires long generation time It performs the calculations for a large set of parton-level processes of interest for the LHC: For example W+1,2,3,4,5 parton samples are generated separately at LO. The interface to HERWIG or PYTHIA provides the treatment of higher-order correction (via parton-showers) and hadronisation. How to avoid double counting between samples and provide predictions for inclusive samples of arbitrary jet multiplicity? Jet-parton matching (MLM prescription) Alessandro Tricoli, RAL

  5. MLM prescription Matching of matrix-element hard partons and shower generated jets, following the so-called MLM prescription: given a jet-algorithm the number of jets must be equal to the number of partons. • Events passing the matching criterion and having extra jets due to the parton shower evolution can be kept (inclusive mode) or rejected (exclusive mode). The inclusive mode must be used only for the sample with the highest parton multiplcity (e.g. the W+5 jet sample in our case). • The set of showered events which survived the matching should be combined to obtain a fully inclusive result. For example for W + up to 5 jets, we generate exlusive W+1 parton, W+2 partons, W+3 partons, W+4 partons and inclusive W+5 partons • Each of the individual samples will have its own absolute normalization. • Since the definition of jet used by the matching prescription will most • likely not coincide with the jet definition used by the user analysis, events from a given • partonic multiplicity will result in events with a spectrum of jet multiplicities. Alessandro Tricoli, RAL

  6. Very stringent generator filter cuts: • 4 jets with pt > 40 GeV • leading jet with pt > 80 GeV • MissEt > 80 GeV Data Sets • SUSY Alpgen + Jimmy: 4 samples • exlcusiveW->en + 2 parton, W->en + 3 partons, W->en + 4 partons inclusive W->en + 5 partons • PDF: CTEQ6LL • Reconstruction ATHENA v12.0.3 • Offline Analysis: AOD/AAN-tuples MLM Matching with CONE R=0.4 Alessandro Tricoli, RAL

  7. e p p n jet W->en +jets Event Selection • First identify events with high Et electron from W decay • W -> en event offline selection: • Standard Electron Identification: isEM and cluster-track matching requirement • cracks removal h=1.37-1.52 and |h|<2.4 • Electron ET>25 GeV • Missing-ET >25 GeV (redundant in this sample due to filter cuts) • No jet veto cuts • Then look at jets in the events • Electron-Jet overlap removal: jets selected if there is no electron within DR<0.4 • Minimum Jet Pt cut: due to high generator filter cuts, choice jet Pt>40 GeV (it will be lowered for SM samples) Assumed luminosity = 100 pb-1 Alessandro Tricoli, RAL

  8. Missing ET Waiting for 12.0.4!!! In 12.0.4 refined calibration available Planning to use a pre-12.0.4 nightly to check the new MissET in the next few days Alessandro Tricoli, RAL

  9. Reconstructed Ele. After offline selection Generated electrons From W decay (no cuts) Electron ET, h distributions Compare reconstructed electrons after the W offline selection cuts to Generator level electrons coming from W decay (no cuts applied at generator level) Bump due to W+2,3 partons events Electron Et (GeV) Electron h • Dependence of electron identification eff. on number of jets • Dependence of the shape of electron distributions on the number of jets Need investigation Alessandro Tricoli, RAL

  10. Trigger efficiencies for W+jet events From Monika • Use 11.0.42 simulated csc files for W+2, 3, 4, 5 jets generated with alpgen • Datasets: 5223, 5224, 5225, 5226 • For comparison use inclusive We generated with Pythia (DS 5104) • Reconstruct using 12.0.3 + TriggerRelease-00-03-88 • Trigger efficiencies for e25i normalised to electron with ET>25GeV in ||<1.37 or 1.52<||<2.47 at MC level Alessandro Tricoli, Oxford University

  11. Trigger efficiencies From Monika • The more jets in the events the more likely the electron is not isolated • L1 isolation rejects those events • EM isolation in ring around 2x2 core ≤ 3 GeV • Had core isolation in 2x2 towers behind EM core ≤ 2GeV • Had isolation in ring around 2x2 core ≤ 2GeV • Also other e-identification cuts (trigger and offline) which look at isolated electrons in ‘smaller’ region partly rejects those events as jet very near Alessandro Tricoli, Oxford University

  12. Redefine: where R=distance parameter Jet Algorithms in Athena 12.0.3 • Fixed CONE: classical fixed cone jet built around seeds • DR=0.4 • Et(seed) > 2 GeV • Et(jet) > 10 GeV • KT clustering: • R=1.0 (default), R=0.4 • Et(jet) > 10 GeV KT jet Algorithm k, l=1,…,n => < are combined into a single object with momentum if and Otherwise is a jet From 12.0.4 KT D=1.0 will be dropped and replaced with KT D=0.6 • The jet clustering algorithms produce jets that are “uncalibrated” i.e at EM scale. • These jets are then calibrated to hadronic scaleand particle level: • Jet-Based Had. Calibration- cell weights applied based on cell energy density and location: compensate and correct signals for e/h ≠ 1 and inactive material losses -> hadronic energy scale jets; • Energy Scale corrections: additional corrections for residual Et and ηdependencies applied i.e noise, algorithm effects etc.-> jets are calibrated to particle level. Alessandro Tricoli, RAL

  13. CONE R=0.4 CONE R=0.4 KT R=0.4 KT R=0.4 KT R=1.0 (AOD Default) KT R=1.0 (AOD Default) Accompanying Jet Multiplicity Jet Multiplicity After W selection cuts Jet Multiplicity After W selection cuts And Jet Pt > 40 GeV Njets Njets • Large discrepancy between Cone and KT algorithms is mainly due to high multiplicity of low Pt jets • at high Pt CONE R=0.4, KT R=0.4 and KT R=1.0 are in better agreement Alessandro Tricoli, RAL

  14. After W selection cuts And Jet Pt > 40 GeV After W selection cuts CONE R=0.4 CONE R=0.4 KT R=0.4 KT R=0.4 KT R=1.0 KT R=1.0 Multiplicity >= Njets Multiplicity >= Njets Accompanying Jet MultiplicityMultiplicity >= Njets Fit lines are exponential Worst fit • Large discrepancy between Cone and KT algorithms is mainly due to high multiplicity of low Pt jets • after min pt cut KT R=1.0 and Cone=0.4 superimpose and are closer to KT R=0.4 Alessandro Tricoli, RAL

  15. CONE R=0.4 KT R=0.4 KT R=1.0 Accompanying Jet ET Jet Et (GeV) • If all jets are considered: KT R=1.0 and KT R=0.4 jets are softer than Cone R=0.4 Alessandro Tricoli, RAL

  16. (a) (a) (a) (b) (b) (b) (c) (c) (c) (d) (d) (d) (e) (e) (e) Highest accompanying Jets (I) KT R=0.4 CONE R=0.4 Jet Et (GeV) Jet Et (GeV) • 1st highest Et jet in W+ >=1 jet • 2nd highest Et jet in W+ >=2 jets • 3rd highest Et jet in W+ >=3 jets • 4th highest Et jet in W+ >=4 jets • 5th highest Et jet in W+ >=5 jets KT R=1.0 We can see the effect of the filter cuts especially in the 2 highest Et jets Jet Et (GeV) Alessandro Tricoli, RAL

  17. CONE R=0.4 CONE R=0.4 KT D=0.4 KT D=0.4 KT D=1.0 KT D=1.0 Highest accompanying Jets (II) 2nd highest Et jet in W+ >=2 jets 1st highest Et jet in W+ >=1 jets Jet Et (GeV) Jet Et (GeV) 3rd highest Et jet in W+ >=3 jets 4th highest Et jet in W+ >=4 jets Jet Et (GeV) Jet Et (GeV) For the highest-ET jets KT jets are harder than Cone, especially KT R=1.0 Alessandro Tricoli, RAL

  18. Detector level Detector level Detector level Matched-Truth Matched-Truth Matched-Truth Truth-Matching Jets: Et distributions CONE R=0.4 KT R=0.4 Jet Et (GeV) Jet Et (GeV) KT R=1.0 Same jet algorithms are applied on final state truth particles, Then a det.-truth matching is performed KT algorithms show better agreement with MC-truth than Cone Jet Et (GeV) Alessandro Tricoli, RAL

  19. (EtREC – EtMatch-Truth ) / EtMatch-Truthexample: W+4partons sample CONE R=0.4 KT D=0.4 Truth Jet Et (GeV) Truth Jet Et (GeV) KT D=1.0 Same jet algorithms are applied on final state truth particles, Then a det.-truth matching is performed KT algorithms show better agreement with MC-truth than Cone Truth Jet Et (GeV) Alessandro Tricoli, RAL

  20. CONE R=0.4 KT R=0.4 Before jet Pt cut Before jet Pt cut Detector level Generator level KT R=0.4 CONE R=0.4 After jet Pt cut After jet Pt cut Njets Njets KT R=1.0 Before jet Pt cut KT D=1.0 KT R=1.0 After jet Pt cut Njets Reconstructed vs Generated Jets: Jet multiplicity Same jet algorithms and cuts applied on reconstructed and generated jets • Good agreement between det. and gen. level multiplicities for higher Pt jets • Better agreement for Cone R=0.4 • and KT R=1.0 algorithms Alessandro Tricoli, RAL

  21. Conclusions and Outlook • We have been developing tools to investigate W->en + jets events • so far applied on SUSY samples • will be applied to the SM samples when available • We applied the W offline selection and compared different jet algorithms (CONE R=0.4, KT R=0.4 and KT=1.0) on jet multiplicity and jet Et. • Detail investigation is needed to estimate systematic uncertainties on jet multiplicity and jet Et • At Tevatron: • uncertainty on jet multiplicity dominates uncertainty on W+jets s • uncertainty on jet multiplicity is dominated by jet energy scale • Further investigation on electron identification in multi-jet environment Alessandro Tricoli, RAL

  22. EXTRAS Alessandro Tricoli, RAL

  23. (a) (a) (a) CONE R=0.4 KT D=0.4 (b) (b) (b) (c) (c) (c) (d) (d) (d) (e) (e) (e) (f) (f) (f) Jet Et (GeV) Jet Et (GeV) KT D=1.0 Jet Et (GeV) Highest accompanying Jets (III) • 3rd highest Et jet in W+ >=3 jets • 4th highest Et jet in W+ >=4 jets • 5th highest Et jet in W+ >=5 jets • 6th highest Et jet in W+ >=6 jets • 7th highest Et jet in W+ >=3 jets • 8th highest Et jet in W+ >=3 jets Alessandro Tricoli, RAL

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