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single top quark production

single top quark production. Ulrich Heintz Brown University. outline. top quark introduction Tevatron and DØ experiment event selection matrix elements boosted decision trees bayesian neural networks cross section and | V tb | other measurements summary. outline.

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single top quark production

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  1. single top quark production Ulrich Heintz Brown University Ulrich Heintz - seminar - Stony Brook

  2. outline • top quark introduction • Tevatron and DØ experiment • event selection • matrix elements • boosted decision trees • bayesian neural networks • cross section and |Vtb| • other measurements • summary Ulrich Heintz - seminar - Stony Brook

  3. outline • top quark introduction • Tevatron and DØ experiment • event selection • matrix elements • boosted decision trees • bayesian neural networks • cross section and |Vtb| • other measurements • summary Ulrich Heintz - seminar - Stony Brook

  4. top-antitop quark pair production • observed first in 1995 by CDF and DØ • “easy” to see Ulrich Heintz - seminar - Stony Brook

  5. top-antitop quark pair production • observed first in 1995 by CDF and DØ • measure strong coupling of top quark • pp = 7.6  0.9 pb • mtop = 173.11.3 GeV • q = +2/3 (tW+b) preferred over -4/3 (tW-b) Ulrich Heintz - seminar - Stony Brook

  6. top quark decay • weak interaction tWb’ b’ = Vtd d + Vts s + Vtb b • tt production • B(tWb) > 0.79 @ 95% CL • |Vtb| >0.89 @ 96% CL • top << experimental resolution • B decays • |Vub|2 + |Vcb|2 + |Vtb|2 = 1 • |Vub|=0.00393, |Vts| = 0.0412  |Vtb| = 0.9991 W t assume unitarity of 33 CKM matrix b Ulrich Heintz - seminar - Stony Brook

  7. single top quark production • weak interaction •   |Vtb|2 • no assumptions on number of generations or unitarity of CKM matrix t-channel s-channel NLO = 1.120.05 pb = 2.340.13 pb Kidonakis and Vogt, PRD 68, 114014 (2003) for mt =170 GeV Ulrich Heintz - seminar - Stony Brook

  8. single top quark production • sensitive to new physics • 4th quark generation • anomalous Wtb vertex • new particles (H+, W’) • FCNC • important benchmark in understanding the backgrounds to Higgs search in WH channel Ulrich Heintz - seminar - Stony Brook

  9. single top quark production t-channel s-channel Ulrich Heintz - seminar - Stony Brook

  10. single top quark production • 2006: D0 announces evidence for single top production DØ Evidence paper PRL “Editor’s Suggestion” 110 SPIRES citations Ulrich Heintz - seminar - Stony Brook

  11. outline • top quark introduction • Tevatron and DØ experiment • event selection • matrix elements • boosted decision trees • bayesian neural networks • cross section and |Vtb| • other measurements • summary Ulrich Heintz - seminar - Stony Brook

  12. the Tevatron • counter rotating beams of protons and antiprotons • radius = 1 km • beam energy = 980 GeV • 21011protons in 36 bunches • 21010 antiprotons in 36 bunches • energy stored in beams = 35 kJ • time for one revolution = 21 s • time between collisions = 396 ns • peak luminosity = 2.81032cm-2s-1 Ulrich Heintz - seminar - Stony Brook

  13. the Tevatron … still the only place to find top quarks CDF DØ 2 km Ulrich Heintz - seminar - Stony Brook

  14. the Tevatron 2.3 fb-1 observation 0.9 fb-1 evidence Ulrich Heintz - seminar - Stony Brook

  15. the DØ detector muon toroid calorimeter beam pipe Ulrich Heintz - seminar - Stony Brook

  16. 19 countries • 80 institutions • 700 physicists D0 Collaboration Ulrich Heintz - seminar - Stony Brook

  17. outline • top quark introduction • Tevatron and DØ experiment • event selection • matrix elements • boosted decision trees • bayesian neural networks • cross section and |Vtb| • other measurements • summary Ulrich Heintz - seminar - Stony Brook

  18. a needle in a hay stack single top dominant background: Wbb, W+jets Ulrich Heintz - seminar - Stony Brook

  19. event selection • electron pT > 15 GeV, || < 1.1 • muonpT > 15 GeV, || < 2.0 • 20 < missing pT < 200 GeV 2-4 jets leading jet pT > 25 GeV, || < 3.4 other jets pT > 15 GeV, || < 3.4 • 1 b-tagged jet • leading b-jet pT > 25 GeV, || < 3.4 24 channels: 2 running periods  2 lepton flavors  3 jet multiplicities  2 b-tag multiplicities Ulrich Heintz - seminar - Stony Brook

  20. event selection • b-jet tagging • b lifetime  1.6 ps • travels a few mm before decaying large impact parameter secondary vertex primary vertex Ulrich Heintz - seminar - Stony Brook

  21. event selection • separate b-jets from light-quark and gluon jets to • reject most W+jets background • neural network algorithm • based on impact parameter and reconstructed vertex • leading b-jet pT > 20 GeV • define two mutually exclusive samples • one tight tag (eb= 40%, ec= 9%, el = 0.4%) • two loose tags (eb= 50%, ec= 14%, el = 1.5%) Ulrich Heintz - seminar - Stony Brook

  22. signal and background models • single top quark production • modeled using SINGLETOP • based on COMPHEP • reproduces NLO kinematic distributions • PYTHIA for hadronization • top-antitop pair production • modeled using ALPGEN • parton-jet matching to avoid double-counting final states • PYTHIA for hadronization • normalized to σ = 7.91pb • Kidonakis and Vogt, PRD 68, 114014 (2003) • uncertainty +7.7% −12.7% (theory, pdf, mtop) Ulrich Heintz - seminar - Stony Brook

  23. signal and background models • W+jets production • modeled using ALPGEN + PYTHIA w/ matching • jet , ,  between leading jets corrected to match data • Z+jets production • modeled using ALPGEN + PYTHIA • Z+ heavy flavor corrected to theory, with ±14% uncertainty • diboson production • modeled using PYTHIA • Normalized to expected cross sections Ulrich Heintz - seminar - Stony Brook

  24. signal and background models • multijet background • jets mimic e,  from semileptonic b-decays • estimates data driven • keep small with selection cuts( 5%) 3/24/2009 Meenakshi Narain 24 Ulrich Heintz - seminar - Stony Brook

  25. background normalization • before b-tagging • iterative fits to data in three variables • lepton pT, MT, and missing pT • subject to constraint • 30% to 54% (multijet), 1.8% to 3.9% (W+jets) • from max difference with 1-variable fit result Ulrich Heintz - seminar - Stony Brook

  26. background normalization • after b-tagging • W + heavy flavor • normalized to theory (MCFM @ NLO) • 1.47 (Wbb,Wcc), 1.38 (Wcj) • empirical correction from two-jet data and simulation • 0.95 ± 0.13 (Wbb, Wcc) Ulrich Heintz - seminar - Stony Brook

  27. event yield (before b-tagging) expected signal backgrounds s:b  1:250 observed acceptance: 3.70.5% (tb) 2.50.3% (tqb) Ulrich Heintz - seminar - Stony Brook

  28. event yield (after b-tagging) expected signal backgrounds s:b  1:20 observed Ulrich Heintz - seminar - Stony Brook

  29. Data/MC comparison (all channels combined) Ulrich Heintz - seminar - Stony Brook

  30. signal and background models 2 jets 3 jets 4 jets pre tag 1 b-tag 2 b-tags Ulrich Heintz - seminar - Stony Brook

  31. signal and background models • test background model in regions dominated by one type of background tt pairs: W+jets: Ulrich Heintz - seminar - Stony Brook

  32. outline • top quark introduction • Tevatron and DØ experiment • event selection • matrix elements • boosted decision trees • bayesian neural networks • cross section and |Vtb| • other measurements • summary Ulrich Heintz - seminar - Stony Brook

  33. matrix elements • method pioneered by DØ for top quark mass measurement • use 4-vectors of all reconstructed leptons and jets • use matrix elements of main signal and background processes • compute a discriminant • define Psignal as a normalized differential cross section: • performed in 2-jets and 3-jets channels only • split sample in high and low HT to improve performance (W+jets and top quark pair dominated regions) Ulrich Heintz - seminar - Stony Brook

  34. matrix elements 2-jet channels 3-jet channels Ulrich Heintz - seminar - Stony Brook

  35. matrix elements • 2-jet channels tb discriminant tqbdiscriminant Ulrich Heintz - seminar - Stony Brook

  36. matrix elements • starting from 2dimensional s vs t-channel discriminant • rebin to ensure enough background events in each bin • re-order bins according to highest-to-lowest signal:background to obtain the 1dim tb+tqbdiscriminant split according to HT HT > 175 GeV HT < 175 GeV Ulrich Heintz - seminar - Stony Brook

  37. outline • top quark introduction • Tevatron and DØ experiment • event selection • matrix elements • boosted decision trees • bayesian neural networks • cross section and |Vtb| • other measurements • summary Ulrich Heintz - seminar - Stony Brook

  38. boosted decision trees • decision trees • widely used in social sciences • idea: recover events that fail a cut • find cuts with best separation between signal and background • repeat recursively on each branch • stop when no further improvement or when too few events left • terminal node is called a “leaf” • decision tree output = leaf purity • adaptive boosting • technique to improve any weak classifier • used with decision trees by GLAST and MiniBooNE • train a tree • increase weight of misclassified events • train again • average over 20 boosting cycles • dilutes discrete nature of output and improves performance Ulrich Heintz - seminar - Stony Brook

  39. boosted decision trees • 64 input variables • rank variables to select the 50 most sensitive variables for each channel • adding more variables does not degrade the performance • reducing the number of variables reduces the sensitivity of the analysis • use 1/3 of all signal and background events as training sample • train 24 trees • e, • 2,3,4 jets • 1,2 b-tags • 2 detector configurations Ulrich Heintz - seminar - Stony Brook

  40. boosted decision trees kinematics jet characteristics angular correlations top reconstruction Ulrich Heintz - seminar - Stony Brook

  41. boosted decision trees • variables Ulrich Heintz - seminar - Stony Brook

  42. boosted decision trees • apply transformation to discriminant to ensure sufficient number of background events in each bin • provides stability in the final cross section measurement Ulrich Heintz - seminar - Stony Brook

  43. outline • top quark introduction • Tevatron and DØ experiment • event selection • matrix elements • boosted decision trees • bayesian neural networks • cross section and |Vtb| • other measurements • summary Ulrich Heintz - seminar - Stony Brook

  44. tqb Network output Wbb Network output Bayesian neural networks • neural networks are nonlinear functions • defined by weights associated with each node • weights are determined by training on signal and background samples • Bayesian neural networks improve on this • average over many networks weighted by the probability of each network given the training samples • less prone to over-training • network structure is less important – can use larger numbers of variables and hidden nodes • for this analysis: • 18-28 input variables in each channel • 20 hidden nodes • 100 training iterations • each iteration is the average of 20 training cycles • backgrounds are combined for training Ulrich Heintz - seminar - Stony Brook

  45. Bayesian neural networks • list of variables (example from one channel) final discriminant after binning transformation similar to BDT Ulrich Heintz - seminar - Stony Brook

  46. outline • top quark introduction • Tevatron and DØ experiment • event selection • matrix elements • boosted decision trees • bayesian neural networks • cross section and |Vtb| • other measurements • summary Ulrich Heintz - seminar - Stony Brook

  47. cross section measurement • verify that calculation methods work as expected using ensembles of pseudo-experiments • select subsets of events from total pool of MC events • randomly sample a Poisson distribution to simulate statistical fluctuations • background yields fluctuated according to uncertainties to reproduce correlations between components from normalization • each pseudo-experiment simulates one DØ experiment Ulrich Heintz - seminar - Stony Brook

  48. cross section measurement • check discriminant in background dominated regions • W+jets: 2 jets, 1 b-tag, HT < 175 GeV • ttbar: 4 jets, 1-2 b-tags, HT > 300 GeV DØ DØ DØ DØ DØ Ulrich Heintz - seminar - Stony Brook

  49. cross section measurement cross section is given by posterior density peak with 68% interval as uncertainty Ulrich Heintz - seminar - Stony Brook

  50. cross section measurement • before looking at the data • how well can we rule out the background-only hypothesis? • fraction of the ensembles without single top signal that give a cross section at least as large as the expected sm value • convert p-value to “expected significance” • from the data • how well do we rule out the background-only hypothesis? • fraction of the ensembles without single top signal that give a cross section at least as large as the observed value • convert p-value to “measured significance” • what cross section do we measure? • how consistent is the measured cross section with the SM? • fraction of the ensembles with SM-signal pseudo-datasets that give a cross section at least as large as the measured value to get “consistency with SM” Ulrich Heintz - seminar - Stony Brook

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