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14 June 20101 Estimation of bbbar for W(→  ) measurement using subtraction method Y.Fang T.Sarangi and Sau Lan Wu University of Wisconsin, Madison Workshop.

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Presentation on theme: "14 June 20101 Estimation of bbbar for W(→  ) measurement using subtraction method Y.Fang T.Sarangi and Sau Lan Wu University of Wisconsin, Madison Workshop."— Presentation transcript:

1 14 June 20101 Estimation of bbbar for W(→  ) measurement using subtraction method Y.Fang T.Sarangi and Sau Lan Wu University of Wisconsin, Madison Workshop on QCD background to W

2 Subtraction method 5/16/20152 XSection/Eff.(A) Cut/Eff.(A) Data (mixed A and B) Obj A Obj B XSection Cut/Eff.(A) Obj A Obj B Yaixs: xsection/Eff.(A) Suppose we have two object (sample/background) A and B, one (Obj. B) is more sensitive to some cut, the other one is not (Obj. A) less sensitive to the cut (as left plot shows). How to estimate each contribution of them assuming there are mixed in data? step 1: Need to know the cut efficiencies on A as we scan the cut -  Eff.(A). step 2: Plot contribution/Eff.(A) (e.g. Xsection/Eff.(A) ) for Data as a function of Cut/Eff.(A). Because A will be more or less flat (right plot), you are supposed to see from data is that the curve is more or less go asymptotically to a flat region as cut being tighter and tighter. step 3: The asymptotic flat value is the contribution of A ( can be derived from the fit of asymptotic function. Step 4: Subtraction of the asymptotic flat value from data, one will expect to get Obj B. Example : W(→  )w(e ) (A) and w(→  )jet (B)

3 Subtraction method 5/16/20153 XSection/Eff.(A) Cut/Eff.(A) Data (mixed A and B) Obj A Obj B XSection Cut/Eff.(A) Obj A Obj B Yaixs: xsection/Eff.(A) Examples : Suppose ABCut AHow to estimate eff. Of A independently Separate ww and wjets W( →  )w(e )w( →  )jet Eff. Of electron id from Z → ee sample Estimate jet veto rate of ttbar in signal regionWWjetsttbarJet veto cut  jets/Zjets Estimate jet veto rate of ttbar in top-box w/o b-tagging wjetsttbarJet veto cut zjets Estimate b-veto cuts assuming b-tagging is not perfect at early data ttbarWjet+ww+ z( →  ) B-tagging cutTag-probe on ttbar Z  /Zjet,  jet/di-jet separationZ ,  jet Zjet, di-jetPhoton id cut z →  or some MC Dependency (early data).

4 Using d 0 to estimate background (bbar) for W(  ) measurement 5/16/20154

5 Application of subtraction method Suppose we can obtain the efficiency of the cut on d 0 ( x>d 0 ) for muon candidate from bbbar. Sample A is supposed to be bbbar. Sample B are whatever left. Scan the cut (x>d 0 ). When the cut goes tighter, a flat region is expected. – Estimate the contribution of bbbar from the flat value. 5/16/20155

6 How to get efficiency of d 0 cut D 0 cut0.0.010.020.030.040.050.060.070.080.09 Eff.0.510430.380.340.320.310.290.270.260.25 Eff. Esti.0.490.410.370.330.310.300.280.270.250.24 5/16/20156  Preselect ion: 1 staco muon with pt>15 GeV. E T miss >20 GeV.  Tag events with signature 1 Lep + 1 jet (back to the lepton with tight cut on btagging SV0 >10). ( bbbar dominant).  Obtain the efficiencies while sliding the cut on d 0.  Apply subtraction method while the efficiencies can be obtained independently. We expect to have 117 events for 1 pb -1 with d 0 cut = 0. Private bbbar sample (777612.PythiaB_b20b20_mu15X) is used. bjet b→μ

7 Result at 1pb -1 5/16/20157 Estimation from the most left point: 2280  98 Truth : 2210

8 What can see with the current data ? 5/16/20158 Total about ~15 nb -1 P Tμ candi>15 GeV P Tμ candi>15 GeV + jet with SV 0 >10 (antikt4Topo) d0d0 Run : 152166-155697 d0d0

9 Are there some events with b(→μ)+bjet signature? 5/16/20159 ΔΦ(b-jet,b-μ) Tens of events available so far. Here the missing ET cut is not applied. This remind me that I may remove the missing ET cut and get the cut efficiency from b(→μ)+bjet control region. Higher statistics is expected.

10 Conclusion We proposed the subtraction method to estimate the contribution of background bbbar for W(→μν) measurement. – Impact parameter(d 0 ) is used as discriminating variable). In order to make the method to be fully data-driven, the efficiency of d 0 for bbar can be measured by tagging a b-jet (SV 0 ). At 1pb -1, the estimated number of bbbar events for W(→μν) channel is 2280±98 consistent with true bbbar 2210. – Should also be feasible with a few hundred nb -1. Plan : – Use the official sample of 108495 (PythiaB_bbmu4X). – Have a look at more data. 5/16/201510


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