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Measurement of sin 2  W via the likelihood method in Zµ + µ -

Measurement of sin 2  W via the likelihood method in Zµ + µ -. EWK dilepton meeting, 03.02.2011 Alessio Bonato, Andrei Gritsan, Zijin Guo, Nhan Tran Johns Hopkins University Efe Yazgan Texas Tech University. Motivation. Measure spin and couplings of a new resonance

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Measurement of sin 2  W via the likelihood method in Zµ + µ -

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  1. Measurement of sin2Wvia the likelihood method in Zµ+µ- EWK dilepton meeting, 03.02.2011 Alessio Bonato, Andrei Gritsan, Zijin Guo, Nhan Tran Johns Hopkins University Efe Yazgan Texas Tech University

  2. Motivation • Measure spin and couplings of a new resonance • In dilepton channel, consider amplitude of some generic particle X with spin J decaying to two fermions Terms suppressed by chirality More details, see arXiv:1001.3396 By studying the angular distributions, we can measure the spin and couplings of particle X

  3. Motivation • By including dilepton mass-dependence, we can improve sensitivity to non-narrow resonances and interference with SM processes: d/(dm*dcos) • The SM already provides testing ground: pp*/Zl+l- • Recall, for the SM Z (J=1): 1 = cV(W) and 2 = cA(W) • In developing the formalism for generic dilepton resonances, we provide a measurement of the SM couplings and the Weinberg angle, sin2W.

  4. Analysis Outline • Use analytic per event likelihood formalism to extract maximal information • Requires probability distribution function, P, of signal and background • RooFit implementation outlined in CMS-AN-2010-351 • Building the likelihood function • DY mass-angle distribution: P (m,cos) • Include partonic luminosities and dilution: P (m,cos,Y) • Include acceptance: P (m,cos,Y)xGacc(m,cos,Y) • Include resolution+FSR: [P (m,cos,Y)  R (m)] x Gacc(m,cos,Y) • Model built at LO, consider (N)NLO MC (data) as correction to measurement More details: http://indico.cern.ch/getFile.py/access?contribId=0&resId=0&materialId=slides&confId=113453

  5. DY process and PDF factorization Desribe the DY process:P (m,cos) Reduces to usual ~ A(1+cos2) + Bcos */Z Differential cross-section depends on PDFs (fa (m,Y)/ fb(m,Y)): Probability Distribution Function for DY process: P (m,cos,Y) *Requires analytical parameterization of PDFs (see backup for more details), using CTEQ6.6 Y m cos *black points: LO Pythia, blue line: probability distribution function

  6. Dilution • Quark direction is ambiguous in pp collisions. • Use Z boost direction, Y, to determine angle, cos. • Dilution term determined analytically from PDFs. Probability Distribution Function including dilution: P (m,cos,Y) Undiluted case Diluted case cos cos *black points: LO Pythia, blue line: probability distribution function

  7. Trigger and Acceptance Acceptance sculpts further the Y and cosdistributions Probability Density Function ~P (m,cos,Y)xGacc(m,cos,Y) Lepton cuts ( < Ymax;pT > pTmin) yield conditions: cos < tanh(Ymax - Y); cos < [1-(2pTmin/m)2]1/2 Gacc(m,cos,Y) Before acceptance/after acceptance Choose pTmin< 25 GeV in the CS frame - covers standard cuts and triggers: pTmin,1 > 20 GeV and pTmin,2 > 7 GeV in the lab frame

  8. Resolution + FSR Account for resolution+FSR via convolution Probability Density Function ~[P (m,cos,Y)  R (m)] x Gacc(m,cos,Y) Assume resolution function,R (m), unknown. Approximated by quadruple Gaussian,R4g(m), for analytical convolution. Parameters obtained from fit of data. Test formalism: take LO Pythia + FSR and do “fast smear” of track parameters. Fit full probability distribution function to the data and obtain R4g(m) parameters from the fit Gen level FSR + smear Convolution of resolution function R4g(m)

  9. Results at LO Putting it all together… Probability Density Function ~[P (m,cos,Y)  R4g(m)] x Gacc(m,cos,Y) Generate 3M events of DY LO Pythia and fit for sin2W Y m cos Fit result: sin2W = 0.2315  0.0011 Compare with generated value: sin2W = 0.2312 Formalism holds together at LO with negligible biases.

  10. Systematics from NLO *Further discussion later

  11. Status Rest of slides dedicated to “new-ish” results and would be slightly altered for pre-approval talks. • So far, analysis steps… • Agreement good at LO and with CMS NLO MC • Implement a blind analysis fit on first data • Next steps • 35pb-1 40 pb-1 improve statistics • Push to the limits! Improve sensitivity and statistics • Loosen phase-space cuts and extend µ acceptance • Understand systematic effects, estimate uncertainty • Goal: statistical error < 0.01 while keeping systematic errors small All results have been integrated into CMS-AN-2011/031

  12. CMS MC and Data • Samples used: • Data: 40 pb-1, Dec22 Re-Reco (processed by Efe) • MC:/DYToMuMu_M-20_CT10_TuneZ2_7TeV-powheg-pythia/ • Standard cuts used in Afb analysis (selections/triggers in backup) • Use tracker-only isolation moving to 40 pb-1 (HCAL issues) • Relax cuts on pT and  of µ± to expand phase space https://twiki.cern.ch/twiki/bin/viewauth/CMS/ForwardBackwardAsymmetryOfDiLeptonPairs We decide to use new loose cuts to provide greatest sensitivity *Bug found w.r.t. last week in data with new loose cuts

  13. µ efficiency With new loose cuts, make a sanity check of µ efficiency: Make full set of cuts on both muons (trigger + reco) and compare Efficiency for < 2.4 Compares favorably with Muon DPG-PH studies: http://indico.cern.ch/getFile.py/access?contribId=2&resId=0&materialId=slides&confId=94653 C. Botta and D. Trocino

  14. Effect of new loose cuts • Start with sample with standard RECO cuts including mass [60,120] and pT(Z) < 25 GeV, except for  and pT • Apply cuts subsequently: (CS), (lab), pT(CS), pT(lab), and see how cuts sculpt distributions. • Want to lose as few events as possible going from CS cuts to lab cuts

  15. Efficiency of new loose cuts Efficiency: look at distributions before and afterHLT, reconstruction, and lab vs. CS cuts Points: gen. level before any cuts Want to see flat efficiency in Y and cos to agree with our model.

  16. Fit results: simulation Fit for sin2W on CMS NLO MC using new loose cuts Looser cuts improve error, but hint of bias Compare with generated value: sin2W = 0.2311 Fit result : sin2W = 0.2283  0.0014

  17. Fit results: data Fit for sin2W on CMS data using new loose cuts Nominal fit floats momentum scale (Z mass) to reduce systematics, more later. Fit results : sin2W = ????  0.0077 mZ = 91.072  0.029

  18. Systematic Uncertainties List of sources of systematics and treatments • ISR and LO model: contributions from NLO suppressed by cut on pT of Z, linear scaling • Variation at level of 0.002, tests statistics-limited, error ~0.001 • Parton Distribution Function uncertainty • First attempt, make same measurement using MSTW2008 PDF set, variation at ~0.001, statistics-limited • More sophisticated methods under investigation

  19. Systematic Uncertainties • Resolution model and FSR: take resolution+FSR from MC and apply it in data • In data, float resolution model parameters in addition - observe difference in central values from nominal fit: 0.0015 • Momentum scale and mis-alignment/calibration • Float Z mass in nominal fit: 91.072 ± 0.029 to reduce sensitivity to momentum scale, in agreement with MuScle corrections • Further systematics by comparing central fit values in data with and without MuScle corrections: 0.0016 • Fit model (efficiency, triggers) • MC fit shows hint of bias, conservatively ~0.003 • Background • Statistical considerations estimate ~0.0006, to do more careful treatment fitting background shapes

  20. Systematic Uncertainties • Some systematics limited by statistics, conservative estimates made, require larger MC sample (currently ~1fb-1 of statistics) • Systematics overlap, correlated, overall estimation of systematic uncertainties convservative • In some cases, simplistic estimate, more detailed study underway

  21. Outlook • Push analysis to the limits, use as much phase space (loose cuts) and statistics (40 pb-1) as possible • Converged on loosest possible cuts • Investigation of systematic uncertainties • Consider ISR and LO model, PDF uncertainties, resolution+FSR model, momentum scale, fit model, and background contributions • Continue further studies on systematics • Finalize statistical tests: toy MC experiments, pulls, and goodness-of-fit Fit result : sin2W = ????  0.0077 (stat.)  0.0044 (sys.)

  22. For reference For a description of the method and documentation please see: http://indico.cern.ch/getFile.py/access?contribId=8&resId=0&materialId=slides&confId=124119 (N.T.) http://indico.cern.ch/getFile.py/access?contribId=7&resId=0&materialId=slides&confId=121960 (N.T.) http://indico.cern.ch/getFile.py/access?contribId=6&resId=0&materialId=slides&confId=114638 (A. Gritsan) http://indico.cern.ch/getFile.py/access?contribId=0&resId=0&materialId=slides&confId=113453 (A. Gritsan) and CMS AN-2011/031

  23. backup

  24. Parton Distribution Functions We fit the data (CTEQ6QL) for u,d,c,s,b quarks and gluons with: Example: Fitu quark parton distribution function, x*fu(x,Q2), for a given value of Q (left); then fit parameters for Q-dependence (right) Fit performed over relevant x range

  25. Trigger/Selection • Triggers (OR of singleMuXX and doubleMu3) • Run 136033-147195: singleMu9 • Run 147196-148107: singleMu11 • Run 148108-149442: singleMu15 • Standard AFB selection • Oppositely charge global & tracker muon • dxy < 0.2 for both muons • HLT trigger matching • Pixel hits >= 1 • Tracker hits > 10 • Normalized 2 < 10 • Muon hits >= 1 • N muon stations > 1 • Isolation: (Tracker+HCAL)/pTµ < 0.15

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