Ecal Calibration 2k+10

1. Ecal Calibration 2k+10 Dasha Savrina, Victor Egorychev & Vanya Belyaev

2. Outline Slides from 18 May 2k+10 Slides from 8 June 2k+10 Prospects Vanya Belyaev

3. Kali • Use 80M of real data 2k+10 • Another ~70M in pipeline. Should we add them? • First pass of Kali run (prepare fmDST) using • LHCBCOND.db_new by Olivier with all known corrections and coefficients • CVS HEAD of Calo/CaloReco + Corrections.py from Olivier • Prs+Ecal corrections are applied “by-hand” at the second run by Kali • takes less than 15 minutes to get 85-90% jobs from Grid (A bit longer during working hours) • And few hours to get the remaining 10-15% Vanya Belyaev

4. Starting point After 9 primary iterations l= (98.98±5.91)% Vanya Belyaev

5. Re-reconstruct After 9 primary iterations l*= (99.49±2.19)% l= (98.51±6.81)% Vanya Belyaev

6. Inner Zone Vanya Belyaev

7. Middle Zone Vanya Belyaev

8. Outer Zone Vanya Belyaev

9. All Zones Vanya Belyaev

10. One more re-reconstruction ? l*= (99.98±0.50)% 917 cells > 0.5% 231 cells > 1% 14 cells > 3% 3 cells > 5% Vanya Belyaev

11. (technical) numbers It scales nicely with number of of cores • fmDST reprocessing: • O(50) Grid jobs, less than 10 minutes each • NTuple processing • 1 primary iteration O(2 hours) @ CAF, 7 cores • Histo projection: O(¾hour) • Histo fitting: O(1¼hour) • 1 “long night” for ~8-9 iterations • The whole cycle around 2-3 days … Vanya Belyaev

12. Summary Part-I Next steps? Add more data? One more re-reconstruction round? Check with h0 ? • Ecal calibration constants have been obtained from 2k+10 data • “Iteration convergency/stability”: rms = 0.3% • Clear improvement in p0 mass resolution has been observed • Note: p0 width is not a parameter for calibration! • The mass of p0 is fine • Tiny bias for outer zone: • b? Vanya Belyaev

13. Compare with Barcelona Vanya Belyaev

14. Outer area: 1-l1/l2 Pattern??? x↔ y flip! Vanya Belyaev

15. Middle area: 1-l1/l2 x↔ y flip! Vanya Belyaev

16. Inner area: 1-l1/l2 Pattern x↔ y flip! Vanya Belyaev

17. Next 14 slides • Test 7 variables ( 2 slides per variable) • Try to find the correlation between difference in Spain & Russian constants and the variables • 3 plots per variable: • Slide 1: the four superimposed distributions of variable: |1-l1/l2|<2% 2%<|1-l1/l2|<5% 5%<|1-l1/l2|<10% 10%<|1-l1/l2| • Slide 2, profile plots: • Left: <variable> as function of 1-l1/l2 • Right: <|1-l1/l2|> as function of variable Vanya Belyaev

18. #LLp0 |1-l1/l2|<2% 2%<|1-l1/l2|<5% 5%<|1-l1/l2|<10% 10%<|1-l1/l2| no dependency? Number of reconstructed p0 in max(Eprs1, Eprs2)<10 MeV category Vanya Belyaev

19. #LLp0 Profiles no dependency? <NLL> <|1-l1/l2|> 1-l1/l2 log10 NLL <NLL> Vanya Belyaev

20. #LGp0 |1-l1/l2|<2% 2%<|1-l1/l2|<5% 5%<|1-l1/l2|<10% 10%<|1-l1/l2| some dependency Number of reconstructed p0 in Eprs1<10 MeV,Eprs2>10 MeV category Vanya Belyaev

21. #LGp0 Profiles some dependency <NLG> <|1-l1/l2|> 1-l1/l2 log10 NLG Vanya Belyaev

22. #GGp0 |1-l1/l2|<2% 2%<|1-l1/l2|<5% 5%<|1-l1/l2|<10% 10%<|1-l1/l2| clear dependency! Number of reconstructed p0 in min(Eprs1, Eprs2)>10 MeV category Vanya Belyaev

23. #GGp0 Profiles clear dependency! <NGG> <|1-l1/l2|> 1-l1/l2 log10 NGG Vanya Belyaev

24. #Sp0 |1-l1/l2|<2% 2%<|1-l1/l2|<5% 5%<|1-l1/l2|<10% 10%<|1-l1/l2| clear dependency! Total Number of reconstructed p0 Vanya Belyaev

25. #Sp0 Profiles clear dependency! <NS> <|1-l1/l2|> 1-l1/l2 log10 NS Vanya Belyaev

26. #entries |1-l1/l2|<2% 2%<|1-l1/l2|<5% 5%<|1-l1/l2|<10% 10%<|1-l1/l2| no dependency??? Total Number of entries in p0 histograms (mainly background) Vanya Belyaev

27. #Sentries Profiles no dependency??? <NSentries> <|1-l1/l2|> 1-l1/l2 log10 NSentries Vanya Belyaev

28. sp0 |1-l1/l2|<2% 2%<|1-l1/l2|<5% 5%<|1-l1/l2|<10% 10%<|1-l1/l2| clear dependency p0 width (sigma) from the fit Vanya Belyaev

29. sp0 Profiles clear dependency <sp0> <|1-l1/l2|> 1-l1/l2 sp0 Vanya Belyaev

30. smassp0 |1-l1/l2|<2% 2%<|1-l1/l2|<5% 5%<|1-l1/l2|<10% 10%<|1-l1/l2| Very clear dependency!!! Error in p0 mass from the fit Vanya Belyaev

31. smassp0 Profiles Very clear dependency!!! <smassp0> <|1-l1/l2|> 1-l1/l2 log10 smassp0 Vanya Belyaev

32. Summary-Part-II • Difference for innermost and outermost cells • Moderate dependency on p0 statistics • “No” dependency on number of entries • Clear dependency on p0 width • The most clear dependency on error in p0 mass • “obvious” • Good sign: “error is reliable” • For small error estimate both methods gives the same result No miracles… Vanya Belyaev

33. Prospects • Re-run Kali on Reco04 data • ~150M stripped events are available • Dasha has prepared fmDST • To be analysed • We would like to get there of three coefficients • For whole sample, for the first half and for the second half • The comparison of these three sets will give us some hints about the internal precision of the method for 79-80M statistics Vanya Belyaev

34. What to do with border cells? • We know from MC that for innermost belt and outermost belt we are systematically wrong (~2-10%) • Apply this correction factors from MC for these cells • *AND* exclude these cells from the definition of Ecalfiducial volume for physics studies • This should preserve the goodness of reconstructed phtoons within fiducialvolume Vanya Belyaev

35. Overall Summary This is average <per-cell> The p0-weighted average is much-much-much better This is not in a contradiction with the hypothesis that numbers from Barcelona are “better” • Ecal is calibrated at some reasonably level • Probably not worse than 2% • MC predictions • agreement between Spain & Russia • For some cells there is disagreement between Spain & Russia • For these cells we expect from MC disagreement between iterative-p0 calibration and true calibration Vanya Belyaev

36. Summary in short: she works Vanya Belyaev