Differential Z Cross Section in the Electron Channel

1. Differential Z Cross Section in the ElectronChannel Bryan Dahmes, Giovanni Franzoni, Jason Haupt, Kevin Klapoetke, Jeremy Mans, Vladimir Rekovic V.Rekovic, Differential xsec Z->ee, EWK Preapproval

2. Outline • Theory and motivation for the analysis • Measurement • Strategy • Efficiencies and acceptance • Bin migration and unsmearing • Errors • Sensitivity to PDF’s • The result • With 32 pb-1 infrozen AN-10-367 and AN-11-029 • Updated results • With 36pb-1 V.Rekovic, Differential xsec Z->ee, EWK Preapproval

3. Motivation for Z shape studies • Parton density functions are critical for all processes at a hadron collider. PDF’s need to be measured from LHC data. • We want to measure themwithZ differential cross sections, Y and qT. Z/Drell Yan Production Changes due primarily to inclusion of ds/dYfrom Tevatron. V.Rekovic, Differential xsec Z->ee, EWK Preapproval

4. Pythia Tunes V.Rekovic, Differential xsec Z->ee, EWK Preapproval

5. Measurement Strategy • Analysis equation: • We conduct two separate analyses: X is either rapidity (Y) or transverse momentum (qT) of Z boson. • This is a Shape measurement. We are not measuring cross section. • Main components of the analysis: • Z • Fast MC • Bin Migration and Unfolding • Error estimation V.Rekovic, Differential xsec Z->ee, EWK Preapproval

6. MeasurementStrategy II In the qT analysis • consider only ECAL electrons with|η|< 2.1 to match muon analysis. In the Y analysis • considerECAL electrons within tracking acceptance |η|< 2.5. • use HF electrons to significantly extend the accessible rapidity range; HF electron ID based on longitudinal and transverse shower shape variables. • Not currently using electrons in ECAL outside the tracker acceptance. ECAL HF V.Rekovic, Differential xsec Z->ee, EWK Preapproval

7. Data for the Analyses • Dataset /EG/Run2010A-Dec22ReReco v1/RECO 2.9 pb-1 /Electron/Run2010B-Dec22ReReco v1/ 29.1 pb-1 An error in a GoodLumi file was found immediately before the pre-approval freeze. As a result, only 32 pb−1, not 36 pb−1, is used in the frozen ANs. • HLT V.Rekovic, Differential xsec Z->ee, EWK Preapproval

8. Z Definitions/Electron Selection • Single electron efficiencies are measured with the tag & probetechnique and framework • Tag = ECAL electron, that passed WP80 and matches to HLT path • Probe = With invariant mass (60-120 GeV)SCluster → GsfElectron → WP80(95)→ HLT V.Rekovic, Differential xsec Z->ee, EWK Preapproval

9. Factorization of Single Electron Efficiencies • Offline electron efficiency can be factorized due to several contributions: • HLT efficiency is measured w.r.t. offline: • For HF there is no trigger nor track requirement: V.Rekovic, Differential xsec Z->ee, EWK Preapproval

10. Single Electron Efficiencies Z-shape measurement is differential in Y, qT. Therefore integral efficiencies don’t suffice. In view of the convolution step they need to be extracted as a function of: pT, ηdet In Tag and Probe, side band background subtraction for single electron efficiency V.Rekovic, Differential xsec Z->ee, EWK Preapproval

11. Efficiency * Acceptance • To extract theefficiency of measured Z as a function of the Z rapidity or Z transverse momentum we start with Z->eeevents from “fast” Monte Carlo and convolve single electron efficiencies. You may want to think about it as MC evaluation of : where X is standing for either Y or qT. . • “Fast” Monte Carlo uses smearing functions on gen level particles (with FSR in a cone) to shift their pT, positions in HF, and to simulate ECAL energy resolution. , V.Rekovic, Differential xsec Z->ee, EWK Preapproval

12. Bin Migration • Xeemeasis not necessarily equal toXeetrue, due to physics and detector effects. • FSR photon can fall outside its cluster soXmeascan be altered. • emission of bremsstrahlung photons, energy loss in the tracker, intrinsic resolution of calorimeter energy and position measurements. • If these effects are uneven across measurement range, the measured spectrum X can be different from the true spectrum, due to events migrating across the bins. We can correct the measurementby unsmearingit using either of the two recipes: • by average response for each bin, measured by ratiowhich in analysis equation replaces by • by migration matrix that accounts for all possible migrations properly weighted. Inverted migration matrix can then used to unfold the final measurement. In caseoflow statistics this artificially introduces large errors. V.Rekovic, Differential xsec Z->ee, EWK Preapproval

13. Fast MC: Data-driven smearing for ECAL/HF • Model energy resolution for MC smearing: For HF σ is of a Gaussian, for EE, EB σ is of a Crystal Ball • Derive smearing parameters by comparing invariant mass of smeared MC (eg colored histograms) to DATA, to Minimize χ2 to obtain function terms. V.Rekovic, Differential xsec Z->ee, EWK Preapproval

14. Fast MCreproduces single electron and di-electron variables that compare well todata Type 1 ECAL-ECAL Z Type 2 ECAL-HF Z Leading Electron PT HF Electron PT ECAL-HF dielectron mass ηe V.Rekovic, Differential xsec Z->ee, EWK Preapproval

15. Eff xAcc of Measured Z qT[GeV] V.Rekovic, Differential xsec Z->ee, EWK Preapproval

16. Unsmearing due to Average Bin Migration Unsmearing for ds/dY Unsmearing for ds/dqT V.Rekovic, Differential xsec Z->ee, EWK Preapproval

17. Systematic Uncertainties • Different sources of systematic errors are considered: • From electron efficiencies • Energy scale • Background subtraction • Uncertainties in the PDF’s used to compute efficiencies give rise to systematics to the measurement significant small V.Rekovic, Differential xsec Z->ee, EWK Preapproval

18. Error from Energy Scale Y qT[GeV] • Two sources of systematatic: • Vary energy scale: +/- 1% EB,+/- 3% EE • Vary local energy scale to account for uncertainty in transparency corrections: • +/- 0.13% |eta| for EB • +/-2 +/- 1.5% |eta| for EE V.Rekovic, Differential xsec Z->ee, EWK Preapproval

19. Background (QCD) Eg: 0.2 < YZ < 0.3 For each bin, fit Mee to SIGNAL + BG shapes. Uncertainty in BG is dominated by statistics. Will decrease with future increased data sample. Extract BG contribution from DATA = SIGNAL + BG • SIGNAL is described with POWHEG smeared Fast MC. • BG sample is the QCD enriched sample obtained by inverting ID cuts:candidates that fail ECAL WP95 (ID or isolation) or HF ID. • For each bin derive nominal line shape of BG, described aswhere V.Rekovic, Differential xsec Z->ee, EWK Preapproval

20. PDF systematic SAMPLE: 200 M events in POWHEG with |ηe| < 2.5 through FastMC, reweighted for 52 PDF CT10w vectors. SAMPLE: 40 M events in POWHEG passed through FastMC, reweighted for 52PDF CT10w vectors. |ηgen,e| < 2.5 • POWHEG + FastMC isused to determine EffxAccfor the measurement. • How much is the uncertainty on EffxAcccoming from used PDF model affecting the uncertainty of the measurement? Is it compromising the sensitivity to PDF constraints? • Ansewer: NO. • impact 0.1% in the central Yregion, and below 0.5% inYmeasurement range • Impact at most 0.6% in qT measurement range V.Rekovic, Differential xsec Z->ee, EWK Preapproval

21. PDF Sensitivities –Can weconstrain PDFs? CT10w consist of 26 vectors, each with +ive and –ive variation Y and qT analysis suggest largest sensitivity to different PDF vectors of CT10w. Largest sensitivity in Y –vector 23 Largest sensitivity in qT–vector 5 CT10w vector 23 CT10w vector 5 Y qT[GeV] V.Rekovic, Differential xsec Z->ee, EWK Preapproval

22. Sensitivities of Y and qT Analyses to CT10w As expected: Y and qT analyses have differentsensitivity to PDF models in CT10w. Maximum sensitivity isabout 3%. V.Rekovic, Differential xsec Z->ee, EWK Preapproval

23. All Errors for Y Analysis Statistics dominated.Largest systematic from BG (stat), which will decrease with more acquired integrated luminosity. NegligiblePDF errors& unsmearing V.Rekovic, Differential xsec Z->ee, EWK Preapproval

24. All Errors for qT Analysis Largest systematic from Energy Scale andBG (stat). The later will decrease with more acquired integrated luminosity. Negligible PDF & unsmearing errors V.Rekovic, Differential xsec Z->ee, EWK Preapproval

25. Result for Y V.Rekovic, Differential xsec Z->ee, EWK Preapproval

26. The Final Result for |Y| V.Rekovic, Differential xsec Z->ee, EWK Preapproval

27. The Final Result for qT (linear) smeared V.Rekovic, Differential xsec Z->ee, EWK Preapproval

28. The Final Result for qT (log) smeared V.Rekovic, Differential xsec Z->ee, EWK Preapproval

29. Updates: Results with 36 pb-1 V.Rekovic, Differential xsec Z->ee, EWK Preapproval

30. Result for Y with 36 pb-1 V.Rekovic, Differential xsec Z->ee, EWK Preapproval

31. The Final Result for |Y|with 36 pb-1 V.Rekovic, Differential xsec Z->ee, EWK Preapproval

32. The Final Result for qTwith 36 pb-1 (linear) V.Rekovic, Differential xsec Z->ee, EWK Preapproval

33. The Final Result for qTwith 36 pb-1 V.Rekovic, Differential xsec Z->ee, EWK Preapproval

34. Conclusions • We performed a measurement of differential cross section in Y and qT of the Z boson in electron channel with 32 pb-1 of 2010 data • Analysesare statistically dominated • Important systematic is on BG estimation which will be reduced withincreased data sample in 2011. • Notes AN-10-367 and AN-11-029 are frozen, but updates are included in this presentation. • We add 4 pb-1 of data with new JSON file released few days before freeze. • Final plots of POHEG prediction in frozen qTAN-11-029 were not unfolded for smearing. The updates with 36 pb-1 presented today include unsmearing. • As a cross check, we measured inclusive cross section, and observedagreement with the result from VBTF • Comparison of data will be discussed in the following talk. V.Rekovic, Differential xsec Z->ee, EWK Preapproval

35. BACK-UP V.Rekovic, Differential xsec Z->ee, EWK Preapproval

36. Single Electron Efficiencies (T&P) This is probably for BACK-UP V.Rekovic, Differential xsec Z->ee, EWK Preapproval

37. Eff xAcc vs. qTwrtMesarued and wrt True Z V.Rekovic, Differential xsec Z->ee, EWK Preapproval

38. Bin migration and unfolding in Y • Migration matrix from the FSR and smearing implemented in fast Monte Carlo • Unfolding matrix obtained by inversion • Systematics from unfolding, less or much less than 1%: • Base unfolding matrix based on smearing parameters • Compare it with results from varying ±1σ the smearing in fast Monte Carlo • Systematic defined as quadrature sum of variations in each bin V.Rekovic, Differential xsec Z->ee, EWK Preapproval

39. Bin migration and unfolding in qT • Migration matrix from the FSR and smearing implemented in fast Monte Carlo • Unfolding matrix obtained by inversion • Systematics from unfolding, less or much less than 1%: • Base unfolding matrix based on smearing parameters • Compare it with results from varying ±1σ the smearing in fast Monte Carlo • Systematic defined as quadrature sum of variations in each bin V.Rekovic, Differential xsec Z->ee, EWK Preapproval

40. Systematic Uncertainty on Bin Migration for Y for average bin unfolding Cumulativesyst error due to Fast MC is 0.2%. For matrix unfolding cumulative syst error due to Fast MC is 0.2%. V.Rekovic, Differential xsec Z->ee, EWK Preapproval

41. Systematics from electron efficiencies: Slide for BACK UP statistical bin correlated qT V.Rekovic, Differential xsec Z->ee, EWK Preapproval

42. PDF errors to Eff x Acc vs qT CT10w400 M POWHEG events CT10w40 M POWHEG events Den =|Y (genZ) < 2 Den = ECAL-ECAL Den = No cut on Y of gen Z V.Rekovic, Differential xsec Z->ee, EWK Preapproval

43. Inclusive Cross Section From ds/dY analysis From ds/dqT analysis V.Rekovic, Differential xsec Z->ee, EWK Preapproval