1. 1 Single top in e+jets channel Outline : - Data and MC samples - Overview of the analysis - Loose and topological cuts - MC efficiencies and expected number of background events - Cross checks - Summary - Issues Emmanuel Busato, Bernard Andrieu and Marumi Kado Goal : Give a first look at single top analysis in the e+jets channel with soft muon tagging  Take RunI analysis as a starting point.  Use results from very recent ttbar analysis (trigger efficiencies, ID efficiencies, soft muon tag efficiencies)  This analysis is described in D0Note 4274

2. 1 Data and MC sample Data (great thanks to Lukas !) :  Root-tuples from Lukas :  92 pb -1  p13.05.00 and p13.06.01  calo corrections from Jan (shared energy problem) is applied  new jet seeding applied  QCD control sample from Lukas (23900 events) : signal sample with no requirements on the electron (not even emIDs) MC :  single top events generated with ONETOP (p14)  Wgfusion (2395 events)  schannel (4291 events)  Wjj (37950 events), Wjjj (44158 events) and Wjjjj (47500 events) generated with Alpgen (p14)  tt (11000 events) generated with Pythia (p14).

3. 1 Overview of the analysis Main backgrounds :  W + jets  QCD Other background considered : tt pair production We define 4 set of cuts : 1. preselection cuts 2. topological cuts 3. soft muon tagging 4. topological cuts + soft muon tagging For each cut, define a loose and a tight sample : tight  loose + electron likelihood (D) Use matrix method to separate Wjets  tt  singletop contributions from QCD contribution (use  qcd and  w estimated by Lukas) Expected number of background events :  Estimate efficiencies to pass cuts in Monte Carlo (except for QCD)  normalize to the number of events found with Matrix Method in preselection sample (except for tt, where we normalize to the measured cross section)

4. 1 Preselection sample definition Trigger : em15_2jt15 (used for tt  e  jets analysis)  MET  15 GeV  1 electron : pT  20 GeV |  det |  1.1 (and [1.1,2.5])  2 nd em object veto  number of jets  2  leading jet : pT  15 GeV |  det |  2.5  second jet : pT  15 GeV (pT  10 GeV) |  det |  2.5  (electron, MET) > 0.5 (not used in RunI)  "triangle cuts" : (20/  ).  (electron, MET) - MET  0 (20/  ).  (jets, MET) - MET  0 (20/  ).  (jets, MET) - MET  20  nb tracks vertex  3  vertex |z|  60 Differences with RunI  red

5. 1 W transverse mass matrix method njets  1 njets  2 njets  3njets  4 njets  1njets  2 njets  3njets  4

6. 1 Data-Monte Carlo comparison jet variables

7. 1 electron and MET

8. 1 Additional cuts Topological cuts :  pT(jet1) + pT(jet2) + pT(electron) + MET  125 GeV  pT(jet1) + 4  MET  155 GeV  pT(jet3) + 5  pT(jet4)  47 GeV !! (  njets  4) b-tagging :  soft muon tagging (muon : medium ;  2  100)  R(muon,jet)  0.5 pT(muon)  4GeV |  (muon)|  2 (RunI : |  (muon)|  1.7) pTrel  0  Require at least one tagged jet

9. 1 Topological variables

10. 1 Some comments  Very good agreement for jet multiplicity  Looking at W transverse mass, we see that the resolution is better in MC. than in data (jacobian peak sharper)  second and leading jet pTs are well reproduced for high pT (>40 – 50 GeV). The agreement is not very good for low pT : data harder than MC  most likely due to hadronic energy reconstruction which needs to be improved  electron is harder in MC  most likely due to estimation of QCD background. Should take into account dependence of  qcd on pT(electron), eta(electron)

11. 1 Event yields 13 29 1004 2246 loose 4  1.87  4.6 11Tagged 2.1  1.21.9  2.8 4 Tagged  topological cuts 77  13491  33 568Topological cuts 269  21715  46 984Preselection N QCD N W+tt+single top tight njets  2 :  qcd  0.2002  0.0057  w  0.792  0.021

12. 1 Trigger efficiencies in MC : fold turn on curves for each em15_2jt15 term (v11) in Monte Carlo events MC efficiencies and expected background (single top eff at RunI : 90-93 % depending on eta of the electron) Correction factor : We want to apply objects ID efficiencies from data to our MC objects  Compute efficiencies in reconstructed MC without electron likelihood  Apply a correction factor these efficiencies, using objects efficiencies found by several tt analyzers :  correction factor =

13. 1 Expected number of signal events Number of single top events : N Wgfusion = 0.24 events (RunI : 0.28 events with 90pb - 1 ) N schannel = 0.15 events (RunI : 0.18 events with 90pb - 1 )

14. 1 Expected number of QCD events  Estimation uses only real data.  We take the number of QCD events in the preselection and apply topological and tagging efficiencies to it  number of QCD events in the final sample   eed a control sample to estimate topological and tagging efficiencies  obtained by inverting H matrix cut N QCD in final sample =   events

15. 1 Expected number of W and tt events  topological efficiencies (and tagging efficiency for tt) are obtained from proportion of events that pass each cut in Monte Carlo W  jets and tt.  tagging efficiency for W  jets :  Use tag rate functions from Florian (thanks !) to estimate tagging efficiency in Monte Carlo W  jets. Florian Beaudette

16. 1 Normalize tt background to the measured cross section :  tt in preselection sample = 17.5  6.1 events  tt in topological sample = 4.8  1.6 events  tt in final sample = 0.6  0.2 events Normalize W+jets background to the number of events in preselection sample : N W in final sample = 4.4  1.2 events

17. 1 Cross checks To any number of events found by the matrix method after a given set of cuts, the various efficiencies determined previously are applied (  column ”Predicted”). The result is compared to the numbers found with the matrix method (  column ”Observed”). QCD : W : Agreement between predicted and observed numbers is good

18. 1 Summary Total background : b= 7.7 events Observed number of events : 4 Limit : 10.2 pb a more conservative limit is found using all lower bounds of b, Luminosity, signal efficiency  Limit : 29.7 pb The expected limit is : 44.6 pb Estimated number of background events :

19. 1 Issues (1)  number of single top events expected after all cuts : 0.4  We can maybe gain in the selection of Ws (em likelihood efficiency low  70%)  change the position of the likelihood cut ?  use another discriminant ?  QCD background : even with a tight cut on the em likelihood (>0.4), there is 35% of QCD events in the final sample  Is there a way to remove QCD events further ? (without degrading signal efficiency !)  An interesting variable to look at is the MET significance

20. 1 MET Significance in the e+jets sample QCD W W  MET sig. cut is powerfull to remove QCD events (more than the standard MET cut)  Would allow to replace the 3 triangle cuts and MET cut by only one cut  Drawback : MET significance is a complicated variable  hard to understand

21. 1 Data- Monte Carlo comparison after presel : no MET cut, no triangle cuts but METSig>5

22. 1 Issues (2)  Another place where we can gain a lot is b-tagging :  use of lifetime taggers to increase sensitivity to the signal (cf Mathieu's talk 10/06/03)  We can probably gain a lot in topological selection too