EbE Vertexing for Mixing

1. EbE Vertexing for Mixing Alex

2. Status • Increased sample’s statistics • Full ~350 pb-1 • D0, D+, K+, K*, ’ • Primary Vertex: • SF robustly sitting around 1.38 • Dependencies (Z,Pt,Si hits,Ntracks) • Effect of hourglass • G3X (no time to show today, but does not seem relevant with current statistics) • Systematics • Comparison of pulls and extraction of a common value: • D0 vs Lxy • Secondary Vertex: • Dependencies (Z,,, Pt,Lxy,ct, ,R,Isolation,Si hits) • Extraction of a common scale factor with systematics?

3. Primary Vertex

4. The tools • Prompt peak • V-truth • V1-V2 • d0/ B Lxy d0

5. Scale Factor from V1-V2 • Fit two independent subsets of ‘primary’ [I.e. non-B] tracks • Measure (x1,y1,z1) and (x2,y2,z2) • Obtain / for x, y and z • Fit core with single gaussian (central value) • Repeat fit with two • gaussians (‘syst.’) • Still using 1.38 • For what follows

6. Is the PVSF ‘universal’?

7. Bottomline… • Scale factor shows no strong dependence on variables probed • Any other variable you want to see? • We could be more accurate but remember that the statistics is limited! • Focus on assessing systematics • Inter-sample • Vary fit model (just like the overall fit) • Will try to improve a little more on statistics (K, D), but mostly aiming at improving systematics

8. We can use the B I.P. pulls as cross check of the Lxy resolution… Impact Parameters • Apply 1.38x and beamline constraint, check what happens: • For instance with D0: EbE: 1.351.15 EbE+SSvtx: 1.261.09 • PV scale factor is not the full story: when you bring down PV, the importance of SV increases • Need to get the SV scale factor right! • REM: even if PVSF=SVSF, we cannot use one common LxySF

9. Impact Parameters & scale factors 1.38x on PV 1.38x on SV

10. Secondary Vertex

11. Scale factor from B decays • Example: BK+ • Fit  to a single vertex • “point”  back to K • Measure Lxy wrt B vertex • Pull is a proxy for a “seconday vertex” pull!   “Two” K B “One” Primary Vertex

12. K K* Lxy(2) Pull K+ Lxy(2) Pull Two gauss. One Gaussian

13. Charm… ’ “3-1” Lxy(2) Pull D+ Lxy(2) Pull Two gauss. One Gaussian

14. ’ can be used in two different ways to probe SV ’ “2-2” Lxy(2) Pull “3-1” Two gauss. One Gaussian “2-2”

15. ..and can be used also to probe the PV scale factor • Prompt production • Lxy wrt EbE is 0! 1.310.02 “1.38” 1.010.04

16. Bottomline for SV • Hidden dependencies! • Detector acceptance? • Kinematics? • Multiplicity? (no: K*) • Figure out which distributions are different • Check dependency! …results in the next pages

17. Pt of two Distributions Lxy Pull Z Ct of ‘one’ one’s Lxy  Of two Isol. (0.7) R Pt one’s tracks  Of two #stereo tracks #L00 tracks  Pt at <0.15 Pt at >0.15 • Detector acceptance (z) pretty similar • No clear difference in Si properties • Kinematics differs ( R Pt) • However…

18. Pulls vs variables in prev. page Lxy Pull Z Pt of two Ct of ‘one’ one’s Lxy  Of two Isol. (0.7) R Pt one’s tracks  Of two #stereo tracks #L00 tracks  • Comparing ’ to average of other samples in each bin • Everything excluded except ct • Why? I can think of possible reasons, but in terms of bugs mostly! WORK IN PROGRESS Pt at <0.15 Pt at >0.15

19. Ct and Lxy Ct(one) distribution Lxy(two) pulls vs ct(B) Lxy distribution Lxy(two) pulls vs Lxy(B) The problem does not show up with prompt objects!

20. Ct(one) distribution D+ Montecarlo vs data Lxy(two) pulls vs ct(B) Lxy(one) distribution Lxy(two) pulls vs Lxy(B) The problem does not show up in montecarlo!

21. ’ data vs D+ MC • They are compatible! • The ‘bug’ affects non-prompt data only!!!

22. Is it in (Lxy) or in Lxy? Lxy mean (Lxy) mean Lxy/(Lxy) mean Lxy(D+) Lxy(D+) Lxy(D+) Lxy width (Lxy) width Lxy/(Lxy) width Lxy(D+) Lxy(D+) Lxy(D+)

23. Same plots for ’ Lxy mean (Lxy) mean Lxy/(Lxy) mean Lxy(D+) Lxy(D+) Lxy(D+) Lxy width (Lxy) width Lxy/(Lxy) width Lxy(D+) Lxy(D+) Lxy(D+)

24. Same plots for MC D+ Lxy mean (Lxy) mean Lxy/(Lxy) mean Lxy(D+) Lxy(D+) Lxy(D+) Lxy width (Lxy) width Lxy/(Lxy) width Lxy(D+) Lxy(D+) Lxy(D+)

25. Secondary Vertex Conclusions, so far • We have enough statistics to study the SV scale factor • Dependency on ct unexpected • Problem shows up ‘only’ in long lived signal in data • it’s a pity that’s what we want to use for our analyses ;) • Semileptonic lifetime? • Working on finding the cause • Montecarlo: It works! • Investigate other variables in data (Impact parameter?) • Probe other samples (D0?) • Possible candidates: • Alignment • Scale factors? • This is the last pending big issue at the moment

26. Moving along the plans for improvements! • Understand beamline parameterization: • Is it modeled correctly • Is it measured correctly  Include our best knowledge of it! • Are secondary vertex pulls ok? • Check with montecarlo truth • Use n-prong vertices (J/K, K+/0, K+/0) • Investigate dependencies (Pt, z,multiplicity, )with full statistics

27. Plan • PV shows a very consistent picture • Finish up PV studies (~days): • re-run some ntuples to get full statistics in all cases • Including K, D • Use also BK background to get a source of studies for prompt Lxy pulls? • SV riddle to be solved! • I am working full steam on this. • Two more weeks according to schedule, to straighten everything out, document and insert in the blessing pipeline.

28. Backup

29. Outline • Current status • What was used for the mixing results • What is the current understanding of Ebe • Plans for improvements • How can we improve?

30. Current status EbE: itearative track selection/pruning algorithm to provide an unbiased estimate of the PV position on an Event-by-Event basis • Hadronic analyses used a flat ~25um beamline! • Possible improvements: • Move to “hourglass” • Move to EbE • EbE + Hourglass • One of the ½ leptonic analyses used this with fixed hourglass parameters Hourglass

31. What do we know about EbE? • Unbiased estimator of PVTX Reasonable (~5%) control of systematics

32. Cross checks using I.P.(B) • Lxy involves three ingredients: • EbE • Secondary vertex • Beamline (in beamline constrained fits) Something funny when beamline is used! Scale factors work! Z dep. Beamline improves pulls! B Lxy d0

33. Time dependence of Hourglass parameters Implementing DB access of time-dependent parameters

34. What do we gain? Euphemism • 15-20% In vertex resolution! • Better control of systematics (hard to evaluate) • Correct EbE resolution (it is not clear that it is correct now) • Red arrow is the effect of 1. Only • Point 2. Affects mostly the green area (tiny ?) • Point 3. Has an effect qualitatively similar to 1., but hard to evaluate

35. Hadronic analysis systematics ct scale factor 0.000 0.024 0.061 0.090 0.144

36. Hourglass parameters from DBProfiles

37. B Relative PV/BV contribution to d0 and Lxy pulls w w Lxy d0 • PV and BV are linear combinations of the same covariances (PV, SV), with different coefficients • Lxy sensitive to the major axis of SV • Relative weight of PV and SV covariances different for Lxy and d0 • Look at: Note:the two Lxy (or d0) pieces do not linearly add to 1!

38. B Relative PV/BV contribution to IP and Lxy pulls Lxy d0 • Not Beam Constrained • Beam constrained • Beam constrained with run-dep. hourglass

39. Bottomline: • SV and PV enter very differently in Lxy and d0 • Relative contribution depends strongly on PV and SV scales • Beam constraint squeezes the PV resolution significantly. Becomes second order on Lxy! • We are in a regime where the SV scale factor is critical! … now let’s get more quantitative!