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Measurement of Single Top Quark s-channel Cross Section at the ATLAS Experiment

Measurement of Single Top Quark s-channel Cross Section at the ATLAS Experiment. 10 th China HEPS Particle Physics Meeting Jie Yu Nanjing University 2008-04-27. Outlines. Introduction S-channel cut analysis and results Multivariate analysis and results Summary. Introduction.

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Measurement of Single Top Quark s-channel Cross Section at the ATLAS Experiment

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  1. Measurement of Single Top Quark s-channel Cross Section at the ATLAS Experiment 10th China HEPS Particle Physics Meeting Jie Yu Nanjing University 2008-04-27

  2. Outlines • Introduction • S-channel cut analysis and results • Multivariate analysis and results • Summary 10th China HEP Particle Physics meeting / Nanjing

  3. Introduction The reasons of doing single-top analysis: • a key particle in the quest for the origin of particle mass. • EW interaction of the top quark is sensitive to many types of new physics. • the only known way to directly measure CKM matrix element Vtb • an important background to many searches for new physics • …… 10th China HEP Particle Physics meeting / Nanjing

  4. time Figue1. (a) t-channel (b) W+t channel (c) s-channel q2 ≤ 0, q2 = M2W, q2 ≥ (mt + mb)2. Where q is the four momentum of W boson three different single top mechanisms in Standard Model: 10th China HEP Particle Physics meeting / Nanjing

  5. The main backgrounds • ttbar events • ttbar l+jets mode • ttbar2l+jets mode (with one lepton lost) • W/Z + jets • di-boson ( like: WWlvjj ) • QCD background ( like: ppbbbar) • For the lack of MC data, we are only using ttbar background till now! 10th China HEP Particle Physics meeting / Nanjing

  6. Process cross section and decay mode (1) process:t-channel s-channel Wt channel ttbar σ(pb):245 ± 27 10.2 ± 0.7 51 ± 9 835 Decay mode and probability: tWb~100% Wl v~2/9( l = electron or muon) Wτv~1/9(τ decays into muon 17.8%, electron 17.2%) Wjj~6/9 10th China HEP Particle Physics meeting / Nanjing

  7. Process cross section and decay mode (2) • t-channel:ppWgtb¯qWbb¯ql v b b¯q • Wt channel:pptW  WWb j j l v b • S-channel: ppW*tb¯Wbb¯ l v b b¯ Final state of the three single top channels : • 2 or 3 jets, 1 or 2 b jet, 1 lepton, with missing energy • Preslection cuts Final state of the signal s-channel : • 2 b jets, 1 lepton, with missing energy • s-channel selection cuts Final state of ttbar events: ttbar:ppttbar  W+ b W- b ¯ l v b j j b ¯  l+ vl- v¯bb¯ τ+τ -v v ¯b b¯ τv b j j b ¯ 10th China HEP Particle Physics meeting / Nanjing

  8. Preselection cuts: • Step0: Triggers , Passed e25i or e60 or mu20i • Step1: One high Pt lepton at least, with electron pt larger than 25GeV/c muon 20GeV/c • Step2: Veto of any 2nd lepton with pt larger than 10GeV/c ΔR>0.4 • Step3: at least 2 high pt jets, pt larger than 30GeV/c • Step4: Veto on the 5th jet with pT(jet)>15GeV/c • Step5: At least 1 btagged high pt jet above 30GeV/c η less than 2.5 • Step6: Missing Energy no less than 20GeV Selection for three single top channels 10th China HEP Particle Physics meeting / Nanjing

  9. Strategy(1): s-channel selection cuts • Step1: two b-tagged jets with pT>30GeV/c • Step2: Veto on any 3rd Jet with pT>15GeV/c • Step3:Total Ht (pT combined jets only): 80<Ht<220 GeV/c • Step4: Seperation between 2 btagged jets: 0.5 <ΔR(b1,b2) < 4.0 ; • Step5: Sum of missing Et and pT of leptons: 60 <mEt+pT(e,u) < 130 GeV/c; 10th China HEP Particle Physics meeting / Nanjing

  10. Total Ht mEt+pT(e,u) • We may find out the separation of the variables is not that distinct • Discriminant variables distributions corresponding to an integrated luminosity of 1 fb-1 10th China HEP Particle Physics meeting / Nanjing

  11. The s-ch cut analysis results • Processes muon channel electron channel nEvt to L= 1 fb-1 • s-channel 2.47±0.12% 1.49±0.10% 46 • s-ch (τ)l 0.71±0.16% 0.64±0.15% • t-channel 0.22±0.04% 0.14±0.03% 84 • t-chan (τ)l 0.04±0.04% 0.00±0.00% • W+t channel 0.10±0.03% 0.08±0.03% 11 • W+t chan (τ)l 0.00±0.00% 0.00±0.00% • ttl+jets 0.09±0.01% 0.08±0.01% 223 • tt(τ)l+j 0.04±0.01% 0.02±0.01% • tte + e 0.34±0.05% • ttμ+μ 0.48±0.06% 150 • ttμ+e 0.33±0.04% • ttμ+τ 0.69±0.05% • tt e +τ 0.54±0.05% 273 • ttτ+τ 0.24±0.04% S/B = 46/741 = 6.2%, S/ √ S+B = 1.64 Not good enough Search for improvement 10th China HEP Particle Physics meeting / Nanjing

  12. Strategy (2): MultiVariate Analysis MVA uses multi-variables as input and get an output which in the most of the cases obtain better separation • Step1: two b-tagged jets with pT>30GeV/c • Step2: Veto on any 3rd Jet with pT>15GeV/c • --------------------------------------------------------------------------- • Using MVA Events selected by steps above the line will be used as MVA input 10th China HEP Particle Physics meeting / Nanjing

  13. MultiVariate data Analysis • Methods in MVA • Rectangular cut optimisation • Likelihood estimator (PDE approach) • Multidimensional likelihood estimator (PDE Range Search approach) • Fisher discriminant • HMatrix approach (2 estimator) • Multilayer Perceptron Artificial Neural Network (three different implementations) • Boosted Decision Trees • RuleFit • … 10th China HEP Particle Physics meeting / Nanjing

  14. No Single Best 10th China HEP Particle Physics meeting / Nanjing

  15. Reference PDF’s is an output of Likelihood for every single event, 1 (signal like) , 0 ( background like) Projected Likelihood Estimator (PDE Approach) Combine probability density distributions to likelihood estimator Output is a likelihood ratio • Assumes uncorrelated input variables 10th China HEP Particle Physics meeting / Nanjing

  16. MVA methods output variables( take Likelihood as an example ) • define likelihood functions specific to suppress each background: yttbar/lepton+jets, yttbar/dilepton, yttbar/τ+lepton, yW+jets, yt−channel • five main background processes ttbarlepton+jet, ttbardilepton, ttbarτ+lepton, W+jets, t−channel • Every event has such five MVA output value , and then we shall apply proper cuts on them 10th China HEP Particle Physics meeting / Nanjing

  17. Note: each yLh(i )use some of the variables as input Variables yttbar/lepton+jets, yttbar/dilepton, yttbar/τ+lepton,yW+jets, yt−channel 10th China HEP Particle Physics meeting / Nanjing

  18. Cut value for each method: MVA method to suppress the Bkg:tt->l+jets,tt->l+l,tt->l+tau,W+jets,t-ch • --- Factory : ----------------------------- • --- Factory :Method: Cut value:Cut value:Cut value:Cut value:Cut value: • --- Factory : --------------------------------------------------------------------- • --- Factory : Likelihood: +0.538 +0.523 +0.525 +0.539 +0.525 • --- Factory : LikelihoodD: +0.004 +0.223 +0.214 +0.019 +0.188 • --- Factory : LikelihoodPCA: +0.592 +0.525 +0.519 +0.600 +0.576 • --- Factory : HMatrix: -0.184 -0.138 -0.153 -0.183 -0.174 • --- Factory : Fisher: +0.051 +0.039 +0.056 +0.057 +0.064 • --- Factory : MLP: -0.242 -0.135 -0.194 -0.001 -0.179 • --- Factory : CFMlpANN: +0.392 +0.379 +0.395 +0.398 +0.380 • --- Factory : TMlpANN: +0.203 +0.355 +0.337 +0.214 +0.546 • --- Factory : BDT: -0.069 -0.115 -0.131 -0.081 -0.093 • --- Factory : BDTD: -0.141 -0.131 -0.072 -0.120 -0.105 • --- Factory : RuleFit: -0.197 -0.221 -0.217 -0.187 -0.227 • --- Factory : --------------------------------------------------------------------- • --- Factory :which correspond to the working point:eff(signal) = 1 - eff(background) 10th China HEP Particle Physics meeting / Nanjing

  19. Cut on no stack histgrams of TMlpANN method Cut here Signal events tend to be more likely in the right side of the figure 10th China HEP Particle Physics meeting / Nanjing

  20. s-ch Vs ttl+jets Cut on stacked histgrams of BDT method s-ch Vs ttdi-lep 10th China HEP Particle Physics meeting / Nanjing

  21. MVA output cut results (1) see:MVA do bring some improvement Number of events are normalized to L=1fb-1 10th China HEP Particle Physics meeting / Nanjing

  22. MVA output cut results (2) Number of events are normalized to L=1fb-1 10th China HEP Particle Physics meeting / Nanjing

  23. TMVA cut efficiency for signal and background 10th China HEP Particle Physics meeting / Nanjing

  24. Combine two or more MVA methods • --- Factory : Inter-MVA overlap matrix (signal): • --- Factory : -------------------------------------------------------------------------------------------------------------------- • --- Factory :Likelihood LikelihoodD LikePCA HMatrix Fisher MLP CFMlpANN TMlpANN BDT BDTD RuleFit • --- Factory :Likelihood: +1.000 +0.798 +0.801 +0.763 +0.746 +0.740 +0.775 +0.771 +0.754 +0.739 +0.756 • --- Factory :LikelihoodD: +0.798 +1.000 +0.862 +0.858 +0.813 +0.791 +0.719 +0.806 +0.755 +0.788 +0.763 • --- Factory : LikelihoodPCA:+0.801 +0.862 +1.000 +0.882 +0.836 +0.815 +0.730 +0.817 +0.778 +0.798 +0.778 • --- Factory :HMatrix: +0.763 +0.858 +0.882 +1.000 +0.887 +0.854 +0.700 +0.866 +0.820 +0.845 +0.820 • --- Factory :Fisher: +0.746 +0.813 +0.836 +0.887 +1.000 +0.867 +0.699 +0.882 +0.829 +0.863 +0.818 • --- Factory :MLP: +0.740 +0.791 +0.815 +0.854 +0.867 +1.000 +0.647 +0.899 +0.873 +0.900 +0.853 • --- Factory :CFMlpANN: +0.775 +0.719 +0.730 +0.700 +0.699 +0.647 +1.000 +0.680 +0.649 +0.634 +0.643 • --- Factory :TMlpANN: +0.771 +0.806 +0.817 +0.866 +0.882 +0.899 +0.680 +1.000 +0.873 +0.882 +0.846 • --- Factory :BDT: +0.754 +0.755 +0.778 +0.820 +0.829 +0.873 +0.649 +0.873 +1.000 +0.872 +0.859 • --- Factory :BDTD: +0.739 +0.788 +0.798 +0.845 +0.863 +0.900 +0.634 +0.882 +0.872 +1.000 +0.845 • --- Factory :RuleFit: +0.756 +0.763 +0.778 +0.820 +0.818 +0.853 +0.643 +0.846 +0.859 +0.845 +1.000 • If two classifiers have similar performance, but significant non-overlapping classifications  check if they can be combined • The combining job is kind of trivial: do cuts on different classifier output! 10th China HEP Particle Physics meeting / Nanjing

  25. Summary • It is no doubt that top quark analysis can lead us to some new physics • MVA methods can positively improve the cut efficiency in our analysis • Now that real data is in the air, we couldn’t be too prepared 10th China HEP Particle Physics meeting / Nanjing

  26. Thank you ! ! 10th China HEP Particle Physics meeting / Nanjing

  27. Backup slides 10th China HEP Particle Physics meeting / Nanjing

  28. Fisher Linear Discriminant Analysis (LDA) • Well known, simple and elegant classifier • LDA determines axis in the input variable hyperspace such that a projection of events onto this axis pushes signal and background as far away from each other as possible • Classifier computation couldn’t be simpler: “Fisher coefficients” F0 centers the sample mean yFi of all NS + NB events atzero • Fisher coefficients given by: , where W is sum CS + CB • Fisher requires distinct sample means between signal and background • Optimal classifier for linearly correlated Gaussian-distributed variables 10th China HEP Particle Physics meeting / Nanjing

  29. 1 input layer k hidden layers 1 ouput layer ... 1 1 1 2 output classes (signal and background) . . . . . . . . . Nvar discriminating input variables i j Mk . . . . . . N M1 (“Activation” function) with: Nonlinear Analysis: Artificial Neural Networks • Achieve nonlinear classifier response by “activating” output nodes using nonlinear weights • Call nodes “neurons” and arrange them in series: Feed-forward Multilayer Perceptron Weierstrass theorem: can approximate any continuous functions to arbitrary precision with a single hidden layer and an infinite number of neurons Three different MultiLayer Per-ceptrons available in TMVA • Adjust weights (=training) using “back-propagation”: • For each training event compare received and desired MLP outputs  {0,1}: ε = d–r • Correct weights, depending on ε and a “learning rate” η 10th China HEP Particle Physics meeting / Nanjing

  30. Boosted Decision Trees (BDT) • A decision tree is a series of cuts that split sample set into ever smaller sets, leafs are assigned either S or B status • Like this phase space is split into regions classified as signal or background • Each split uses the variable that at this node gives the best separation • Some variables may be used at several node, others may not be used at all 10th China HEP Particle Physics meeting / Nanjing

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