1. LHCb PatVeloTT Performance Adam Webber

2. Why Upgrade?  Currently we de-focus the beams o LHCb Luminosity ~ 2x10 32 cm -2 s -1 o ~ 1 interaction per bunch crossing o Design Luminosity ~ 10 34 cm -2 s -1  Upgrade Luminosity ~ 2x10 33 cm -2 s -1 o Cleverer trigger o More sensitive detector 2

3. The Detector Single arm forward spectrometer 3

4.  Sector: a set of sensors connected to the same readout chip  Split into 1, 2, 3 and 4 sensor long sectors Trigger Tracker (TT) 4

5. Upgrade Project 1: TT Granularity  Sector Y-Granularity ~ tens cm  4 layers – 2 at 5º rotation  PatVeloTT: VELO 5

6. First Results  Two luminosities have been investigated, the initial LHCb target of 2x10 32 cm -2 s -1 and the upgrade target of 2x10 33 cm -2 s -1. Initial studies have looked at the following questions: How many candidate VeloTT tracks are there? How many candidate VeloTT tracks are there? How often do we get it right? How often do we get it right? What is the (1/Pt) resolution of the ‘best’ track? What is the (1/Pt) resolution of the ‘best’ track? How do the Χ 2 of the ‘best’ tracks compare to the rejected ones? How do the Χ 2 of the ‘best’ tracks compare to the rejected ones?  The following graphs are made from simulated Bs-> ϕϕ events using the Minimal Upgrade Layout. Two luminosity data sets: 2x10 32 cm -2 s -1 – 3,430 events: 140,258 VELO tracks 2x10 32 cm -2 s -1 – 3,430 events: 140,258 VELO tracks 2x10 33 cm -2 s -1 – 1,808 events: 178,620 VELO tracks 2x10 33 cm -2 s -1 – 1,808 events: 178,620 VELO tracks 6

7. Track Candidates 2x10 32 cm -2 s -1 2x10 33 cm -2 s -1 7  In PatVeloTT, each VELO track will have a number of candidate tracks associated with it.  Each candidate track will be reconstructed from a set of clusters in the TT.

8. Track Candidates - Sectors (the error bars are smaller than the coloured markers)  Here is the mean number of candidates from the tracks which went through sectors of a particular length. The average number of candidate tracks is considerably larger nearer the beam pipe (where the sectors are smaller). 8

9. Track Candidates - Pt  Here is the average number of candidate tracks for a range of transverse momentum (of the MC particle associated with the VELO track. The points represent the centre of the Pt bins (i.e. 0-0.5 GeV is at 0.25GeV, etc). 9

10. Number of Clusters 2x10 32 cm -2 s -1 2x10 33 cm -2 s -1 10  In PatVeloTT, each VeloTT track will have a number of TT clusters associated with it.  Both luminosities peak at 4 clusters. The higher luminosity distribution has a larger fraction of tracks with 5-7 hits.

11. Correct Clusters? - Pt  It is of interest how often we correctly pick the right clusters when reconstructing a track. Using MC information we can see how many of the clusters associated to the track are correct. The below plot shows how the match percentage varies with Pt. 11

12. Correct Clusters? - Pt  What happens when we make a cut on the reconstructed Pt?  Reconstructed Pt > 1 GeV: 12

13. Correct Clusters? - Pt  Also for reconstructed Pt > 1.5 Gev: 13

14. Correct Clusters? - Sectors  We can also look at how the match percentage varies with sector length. 14

15. Correct Clusters? - Sectors  Reconstructed Pt > 1 GeV: 15

16. Correct Clusters? - Sectors  Reconstructed Pt > 1.5 GeV 16

17. Success Rate – 2x10 32  A successful match is defined as when either: At least 70% of the TT cluster hits are matched to MC truth. At least 70% of the TT cluster hits are matched to MC truth. All but one of the TT hits is matched to MC truth (i.e. 2/3). All but one of the TT hits is matched to MC truth (i.e. 2/3). 17

18. Success Rate – 2x10 33 18  The fraction of unsuccessful cluster matches is much higher at 2x10 33.

19. Success Rate  The mean success rates for the various cuts on the reconstructed Pt: 19

20. 1/Pt Resolution  Resolution of (1/Pt) = [(1/Pt) MC – (1/Pt) measured ] / (1/Pt) MC  This was plotted for multiple bins of Pt ranging from 0-4GeV (see below examples). 20

21. 1/Pt Resolution  The widths of the Gaussians (from each of the Pt bins) was measured and is shown below as a function of Pt.  Statistics were low for the higher Pt bins, hence the large errors. 21

22. Χ 2 of ‘Best’ and Rejected Tracks 2x10 32 cm -2 s -1 2x10 33 cm -2 s -1  Below are plots of the pseudo χ 2 for the ‘best’ track against the rejected tracks. This pseudo χ 2 should be treated as a quality parameter rather than a regular statistical χ 2.  There is a cut on the pseudo χ 2 of the best track at 10 4. The majority of the points on these graphs are located very close to zero. Over the next few slides we will look at particular regions of these graphs. 22

23. Χ 2 of ‘Best’ and Rejected Tracks 23

24. Χ 2 of ‘Best’ and Rejected Tracks 2x10 32 cm -2 s -1 2x10 33 cm -2 s -1 Conditions:  Discarded Tracks pseudo χ 2 < 10 4.  Both best and discarded pseudo χ 2 > 400. 24

25. Χ 2 of ‘Best’ and Rejected Tracks 2x10 32 cm -2 s -1 2x10 33 cm -2 s -1 Conditions:  Both best and discarded pseudo χ 2 < 50. 25

26. Χ 2 Distributions  Mean pseudo χ 2 for all tracks and for tracks with a successful MC cluster match: 26

27. Χ 2 Distributions  The pseudo χ 2 for tracks with an unsuccessful MC cluster match is considerable larger: 27

28. Extras: Hi Steve 08/02/10:  In PatVeloTT the candidate tracks are cut first on how many layers of the TT have cluster hits in them, and then on pseudo χ 2. I thought it might be interesting to see how the pseudo χ 2 compare when: # of layers with hits is equal for the ‘best’ and discarded candidates. # of layers with hits is equal for the ‘best’ and discarded candidates. # of layers with hits is larger for the ‘best’ track. # of layers with hits is larger for the ‘best’ track.  This is shown on the following slides…  Also, I seem to have accumulated millions of graphs… if there’s anything else that you can think of which you’d like to see then there’s a good chance I’ve already made it. Otherwise I’m sure I can make it for you, let me know. 12/02/10:  I’ve tried making the 1/Pt resolution graphs for different cluster MC match fractions (as you suggested), but the statistics aren’t there for an analysis. I’ve included a couple of graphs (slides 34-35) for 100% cluster match and for 75%-87.5% match. These were the only two for which there were enough tracks to make sensible graphs (there was also enough 0% matches but these were nonsensical). 28

29. Χ 2 of ‘Best’ and Rejected Tracks 2x10 32 cm -2 s -1 2x10 33 cm -2 s -1 Conditions:  Number of layers with cluster hits is equal.  Discarded Tracks pseudo χ 2 < 10 4. 29

30. Χ 2 of ‘Best’ and Rejected Tracks Conditions:  Number of layers with cluster hits is greater for the ‘best’ tracks.  Discarded Tracks pseudo χ 2 < 10 4. 2x10 32 cm -2 s -1 2x10 33 cm -2 s -1 30

31. 1D χ 2 distribution examples (see slide 28) 31

32. 1/Pt Resolution Cluster Match % = 100% 32

33. 1/Pt Resolution 75% < Cluster Match % < 87.5% 33

34. 1/Pt Resolution  1/Pt resolution plots were also made by ‘binning’ the data in equal chunks of 1/Pt. This covers the same range of Pt as the previous plot (0 - 4 GeV). (please note that the furthest two points on the right actually contain data from 0 – 100 MeV. But how do you plot a point midway between 0.01 and ∞ ?!) 34