10th China HEPS Particle Physics Meeting 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

10th China HEP Particle Physics meeting / Nanjing Outlines Introduction S-channel cut analysis and results Multivariate analysis and results Summary 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing 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 …… 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

three different single top mechanisms in Standard Model: 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 time 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing 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! 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing 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 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing 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+ v l- v¯bb¯ τ+ τ - v v ¯ b b¯ τv b j j b ¯ 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing Preselection cuts: Selection for three single top channels 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 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing 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; 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing 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 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

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 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing 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 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

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 … 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

No Single Best    / Methods Criteria Performance Training Speed Cuts Likelihood PDERS/ kNN HMatri Fisher MLP BDT RuleFit SVM Performance no / linear correlations   nonlinear correlations  Speed Training Response / Robustness Overtraining Weak input variables Curse of dimensionality Clarity No Single Best 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

Projected Likelihood Estimator (PDE Approach) Combine probability density distributions to likelihood estimator Reference PDF’s Output is a likelihood ratio is an output of Likelihood for every single event, 1 (signal like) , 0 ( background like) Assumes uncorrelated input variables 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

MVA methods output variables ( take Likelihood as an example ) five main background processes ttbarlepton+jet, ttbardilepton, ttbarτ+lepton, W+jets, t−channel define likelihood functions specific to suppress each background: yttbar/lepton+jets, yttbar/dilepton, yttbar/τ+lepton, yW+jets, yt−channel Every event has such five MVA output value , and then we shall apply proper cuts on them 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing Note: each yLh(i ) use some of the variables as input Variables yttbar/lepton+jets, yttbar/dilepton, yttbar/τ+lepton,yW+jets, yt−channel 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

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 : which correspond to the working point: eff(signal) = 1 - eff(background) 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

Cut on no stack histgrams of TMlpANN method Cut here Signal events tend to be more likely in the right side of the figure 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing s-ch Vs ttl+jets Cut on stacked histgrams of BDT method s-ch Vs ttdi-lep 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing MVA output cut results (1) channel\classifiers Likelihood LikelihoodD LikelihoodPCA HMatrix Fisher sch-cut s-channel 44.7 40.1 39.4 41.2 35.8 46 t-channel 52.4 36.5 28.6 30.2 41.3 84 W+t channel 9.4 5.0 7.2 10.5 11 tt-->l+jets 174.7 114.7 112.3 93.6 137.3 223 tt-->di-lep 92.0 67.1 67.9 55.4 62.4 150 tt-->l+tau 198.1 141.2 135.7 119.3 129.5 273 all BKG 526.6 364.5 351.7 307.9 381.0 741 S/B 8.5% 11.0% 11.2% 13.4% 9.4% 6.2% S/sqrt(S+B) 1.87 1.99 2.21 1.75 1.64 see:MVA do bring some improvement Number of events are normalized to L=1fb-1 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing MVA output cut results (2) channel\classifiers BDT BDTD MLP CFMlpANN TMlpANN RuleFit sch-cut s-channel 50.3 46.0 36.0 3.7 40.0 35.0 46 t-channel 39.7 15.9 19.0 9.5 17.0 31.7 84 W+t channel 6.1 6.6 4.4 3.3 8.2 11 tt-->l+jets 88.9 64.7 70.2 15.6 45.2 104.5 223 tt-->di-lep 48.4 29.6 35.1 5.5 23.4 150 tt-->l+tau 75.7 74.1 60.1 97.5 273 all BKG 287.6 192.5 205.0 58.4 149.0 290.3 741 S/B 17.5% 23.9% 17.6% 6.3% 26.8% 12.1% 6.2% S/sqrt(S+B) 2.74 2.98 2.32 0.47 2.91 1.94 1.64 Number of events are normalized to L=1fb-1 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

TMVA cut efficiency for signal and background channel\classifiers Likelihood LikelihoodD LikelihoodPCA HMatrix Fisher sch-cut signal efficiency 1.74% 1.56% 1.53% 1.60% 1.39% 1.79% t-chan efficiency 0.10% 0.07% 0.05% 0.06% 0.08% 0.16% W-tchan efficiency 0.03% tt-->l+jets efficiency 0.04% tt-->di-lep efficiency 0.23% 0.17% 0.14% 0.15% 0.37% tt-->l+tau efficiency 0.39% 0.28% 0.27% 0.24% 0.26% 0.54% channel\classifiers BDT BDTD MLP CFMlpANN TMlpANN RuleFit signal efficiency 1.95% 1.79% 1.40% 0.14% 1.55% 1.36% t-chan efficiency 0.08% 0.03% 0.04% 0.02% 0.06% W-tchan efficiency 0.05% tt-->l+jets efficiency 0.01% tt-->di-lep efficiency 0.12% 0.07% 0.09% tt-->l+tau efficiency 0.21% 0.15% 0.19% 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

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! 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing 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 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing Thank you ! ! 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing Backup slides 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

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 at zero 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 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

Nonlinear Analysis: Artificial Neural Networks Achieve nonlinear classifier response by “activating” output nodes using nonlinear weights Call nodes “neurons” and arrange them in series: 1 i . . . N 1 input layer k hidden layers 1 ouput layer j M1 Mk 2 output classes (signal and background) Nvar discriminating input variables (“Activation” function) with: 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 11/11/2018 10th China HEP Particle Physics meeting / Nanjing

10th China HEP Particle Physics meeting / Nanjing 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 11/11/2018 10th China HEP Particle Physics meeting / Nanjing