1. 1 Andrea Bangert, ATLAS SCT Meeting, 18.05.2007 Monte Carlo Studies Of Top Quark Pair Production Andrea Bangert, Max Planck Institute of Physics, CSC T6 Meeting, 12.06.2007

2. 2 Overview Event samples Selection cuts Cross section studies: Selection efficiencies Dependence of σ tt→jjblνb on jet algorithm parameters. Top mass studies: Dependence of m jjb on jet algorithm parameters. Other observables: Transverse mass of W→lν Transverse mass for t→Wb→lνb H T

3. 3 Event Samples Semileptonic and dileptonic ttbar events csc11 #5200, σ = 461 pb, m t = 175 GeV. Used ~10% of sample as “data”. Used remaining ~90% of sample as “Monte Carlo”. L data = 97 pb -1, N data ~ 45,000 L MC = 973 pb -1, N MC ~ 450,000. Hadronic ttbar events, m t = 175 GeV csc11 #5204, MC@NLO/Herwig σ = 369 pb, L = 77 pb -1, N ~ 30,000. W+N jets events Rome #3017, Alpgen σ = 1200 pb, L = 142 pb -1, N ~ 170,000. Generated using Athena 10.0.2. All samples were reconstructed with Athena 11.0.42.

4. 4 Selection Cuts Exactly one e or μ: E(e) ∆R=0.45 < 6 GeV E(μ) ∆R=0.20 < 1 GeV p T (l) > 20 GeV, |η| < 2.5 (isEM==0) for electrons At least four jets: |η| < 2.5 Three leading jets: p T (j) > 40 GeV Fourth jet: p T (j 4 ) > 20 GeV or p T (j 4 ) > 40 GeV ∆R min (j,e) > 0.4 Missing E T > 20 GeV No b-tagging was required. csc11 #5200 E(e) ∆R=0.45 E(μ) ∆R=0.20

5. 5 Monte Carlo Cross Section Studies Only semileptonic and dileptonic ttbar included. No hadronic ttbar or W+N jets background. ~90% of #5200 used as MC, L = 973 pb -1 → ε MC. ~10% of #5200 used as “data”, L = 97 pb -1. Assume ε data = ε MC. For semileptonic ttbar events with l = e, μ: σ tt ·Γ tt→lνbjjb = (N e data / L data ε e MC ) + (N μ data / L data ε μ MC ) k T (D=0.4) p T (j 4 ) > 20 GeV

6. 6 Efficiencies (L = 973 pb -1 )

7. 7 Cross Section Studies ( L = 97 pb -1 ) Shown is σ tt · Γ tt →l νbjjb (l = e, μ) I and II represent two independent “data” samples. From Monte Carlo: σ tt · Γ tt→lνbjjb = 248 pb L MC = 973 pb -1, L data = 97 pb -1 → δε MC << δN data Only statistical uncertainty due to δN data is shown. “Measured” cross section shows dependence on jet reconstruction algorithm.

8. 8 Top Quark Mass Hadronic ttbar and W+N jets background included. Event reconstruction: Consider selected events. Consider all jets with p T > 20 GeV. Create all possible 3-jet combinations. Select 3-jet combination with maximum p T to represent the reconstructed hadronic top quark. Fit mass distribution using Gaussian and polynomial. Mean of Gaussian is fitted top mass. k T (D=0.4) p T (j 4 ) > 20 GeV

9. 9 Top Quark Mass [L = 97 pb -1 ] Generated top mass is: m t = 175 GeV Reconstruction was performed in 11.0.42: jet calibration known to be flawed. m jjb [in GeV] is given with statistical error on fit from Migrad. Uncertainty of reconstructed top mass due to jet algorithm δm t ~ 2.5 GeV.

10. 10 Transverse W Boson Mass m T W = √ { [E T (l) + E T (v)] 2 – [p T (l) + p T (v)] 2 } m T (W → lv) Ratio of signal to W+N jets background Should we cut at m T (W) < 150 GeV? k T (D=0.4) p T (j 4 ) > 20 GeV

11. 11 Transverse Top Mass m T (t) = √ { [E T (l) + E T (v) + E T (b)] 2 – [p T (l) + p T (v) + p T (b)] 2 } Should we cut at m T (t →l νb) < 300 GeV?

12. 12 HTHT H T = √ [ MET 2 + E T (l) 2 + Σ j E T (j) 2 ] Should we cut at H T < 400 GeV?

13. 13 Conclusion, Next Steps Selection efficiencies are higher for k T (D=0.4) than Cone4. Dependence of efficiencies on jet algorithm → dependence of “measured” σ tt→lvbjjb on jet algorithm. Uncertainty of reconstructed top mass due to jet algorithm δm t ~ 2.5 GeV. m T (t), m T (W), and H T may be useful in selection. 11.0.42 → 12.0.6 Implement analysis in TopQuarkAnalysis package: atlas-sw.cern.ch/cgi-bin/ viewcvs-atlas.cgi/groups/ MPP/TopQuarkAnalysis Athena 12.0.6 Preliminary

14. 14 Backup Slides

15. 15 Successive Combination vs. Cone Algorithm Successive combination algorithm recursively groups objects (particles, calorimeter cells, towers, clusters) with “nearby” momenta into larger sets of objects. “Nearby” is defined in (E T, η, φ ) space. Initial sets contain one object each; final sets are the jets. Never assigns a single object to more than one jet. Merging jets is not an issue: the algorithm performs this automatically. Geometry of jet boundaries can be complicated. Does not yield regular shapes in η- φ plane. Jet cross sections are likely to exhibit smaller higher-order and hadronization corrections. S. Ellis, D. Soper, CERN-TH.6860/93 Cone algorithm defines jet as set of objects whose angular momentum vectors lie within a cone centered on the jet axis. Jet cones can overlap such that one object is contained in more than one jet. Merging jets is an issue. Cone jets always have smooth, well defined boundaries. Jet cross sections may have larger higher-order perturbative corrections. Parameters can be adjusted such that the inclusive jet cross section resulting from application of successive combination algorithm is essentially identical to inclusive jet cross section obtained by applying cone algorithm.

16. 16 k T Jet Algorithm A successive combination algorithm such as k T recursively combines objects (particles, calorimeter cells, towers, clusters) with “nearby” momenta into jets. “Nearby” is defined in η- φ space: ∆R = √((∆η) 2 + (∆ φ) 2 ) Two objects will be merged into one jet if ∆R < D 2. The parameter D must be chosen properly for the process in question. If D is too small, objects which should be combined into a jet may be excluded. If D is too large, objects which should not be merged into a single jet may be merged. D = 0.4D = 0.7D = 0.9D = 1.0D = 0.1 Invariant Mass Distribution of the W Boson Jet reconstruction algorithm was run on Monte Carlo truth after hadronization. No detector simulation was performed.

17. 17 Jet pT p T of Leading Jetp T of Jet 4

18. 18 m T (W) and H T at CDF CDF: hep-ex/0607035

19. 19 Reconstruction Consider all jets with p T > 20 GeV. Construct all possible three-jet combinations. Combination with maximal p T represents t→Wb→jjb. Reconstruct m T (W→lν). m T W = √ { [E T (l) + E T (v)] 2 – [p T (l) + p T (v)] 2 } Form all possible combinations of each remaining jet with W→lν. m T (t) = √ {[E T (l) + E T (v) + E T (b)] 2 – [p T (l) + p T (v) + p T (b)] 2 } Combination with maximal p T represents t→Wb→lνb. Jet chosen to complete t→lνb represents default b jet.

20. 20 Statistical Error on ε and σ Error on efficiency: δ ε = √(ε (1- ε) / N i ) δN e = √N e, δN μ = √N μ δσ e = δN e / L data ε e δσ μ = δN μ / L data ε μ δσ = √(δσ e 2 + δσ μ 2 )

21. 21 isEM: Quality Control for Electrons The isEM flag is designed to identify electrons and reject jets. Represents result of combinations of cuts imposed on quantities reconstructed within: Electromagnetic and hadronic calorimeters: Very little hadronic leakage (1 st bit). Energy deposit in electromagnetic calorimeter is narrow in width (2 nd bit). Energy deposit in electromagnetic calorimeter has one narrow maximum, no substructure (3 rd bit). Inner detector: At least nine precision hits from pixel detector and semiconductor tracker; small transverse impact parameter. η and φ of track are extrapolated to calorimeter cluster; extrapolated and measured values are required to match. Energy measured in electromagnetic calorimeter is required to match momentum measured in inner detector. Requiring isEM==0 demands that each electron candidate pass all of above cuts before being accepted.