1. Systematic errors associated with PID Milind V. Purohit BaBar Analysis Tools Workshop October, 2005

2. 2 Milind V. Purohit, Univ. of South Carolina The PID Systematic Error Issue  The majority of BaBar analyses use some sort of particle ID  Systematic errors associated with the PID efficiency are necessary  There is no prescribed way to obtain these  The need for precise efficiencies increases with time; e.g., upcoming CP violation studies in charm decays will need sub-1% particle ID efficiency errors.

3. 3 Milind V. Purohit, Univ. of South Carolina What is being done today  To understand better the current situation, we can look at recent analyses. A quick scan of ~50 analyses (BAD notes) describing recent analyses (starting from the Pub Board’s 2005 summer papers list) for PID systematics information shows that systematic errors are based on: o Data-MC comparisons o Using PID weight statistics o Using PID killing vs no killing o Other methods o Unclear or no explanation

4. 4 Milind V. Purohit, Univ. of South Carolina Summary of Some PID Systematic Error Determinations Available at http://www.slac.stanford.edu/~purohit/internal/PidSyst.html e (%)mu (%)pi (%)K (%)p (%)BAD #sMethod, Notes 0.5 1213, 824, 1213Data vs. MC: Control Sample vs. PID group efficiencies 5 1205, 971Data - MC comparison. Depends on D0 mass cut. 1.3 1259, 1184Data - MC comparison. 3.5 967, 1076Data - MC comparison. 1 1077, 664Data - MC comparison. 0.5 1137, 824Data - MC comparison. 0.7 - 3.81075, 938PID weight statistics. 0.490.75 1179, 825PID weight statistics. 2.2 1147, 323PID weight statistics. 3.0 1159, 1013PID weight statistics. 23 2 1255, 1214PID weighting. Using PID weight statistics? 55 5 1187, 542PID killing vs. No PID killing. 1.02.00.7 1105, 1032PID killing vs. No PID killing. 3.51107, 1071PID killing vs. No PID killing. 3.0 1129, 1044PID killing vs. No PID killing. 1 1239, 1088PID effect cancels, but accounts for various algorithms. 1.1 1243, 818 Giampiero Mancinelli's study. See http://www.slac.stanford.edu/BFROOT/www/Organization/ CollabMtgs/2001/detDec2001/Wed1a/giampi.pdf 22 2 658"Based on data-MC comparisons." 0.5 1077, 664Entire hadronic Mis-ID rate. 3 1135, 94Different running periods. 5.2 1271, 1203Unclear. 1.0 1027, 768Unclear. 2.0 1037, 902"Common BaBar practice." 0.2 - 2.0 1225, 1146Unclear. 2.6 1243, 818No explanation. 2 1073, 697"[As in] similar analyses." 20 1077, 664Arbitrary. 2.03.0 1161, 1091No explanation. 1 1107, 1071No explanation.

5. 5 Milind V. Purohit, Univ. of South Carolina Summary of current situation  Data-MC comparisons: may simply be validating the simulation, as opposed to providing a real systematic  Use PID weight statistics: certainly a good idea, but is it sufficient?  Use PID killing vs. no killing: a variation of Data-MC comparisons  Other methods etc.: over-estimates, guess-timates, appeals to “common knowledge” and no explanation

6. 6 Milind V. Purohit, Univ. of South Carolina What should be done and how can the PID group help?  The PID group’s PID efficiencies should come with both statistical and systematic error estimates  The best way to estimate PID systematic errors is: (Fill in the blanks here)  If we knew the preferred technique, we would work on implementing it. Your input and work is needed!

7. 7 Milind V. Purohit, Univ. of South Carolina An example of work on PID systematics  A South Carolina student, Ryan White, is trying to address some of these issues:  Compare efficiencies obtained by different techniques: o Compare MC truth efficiency to standard PID efficiencies and try to understand differences. o Question: are differences due impurities and differences in samples?  Compare efficiencies for kaons obtained from different sources: o Compare kaon efficiency for kaons from D0s to kaons not from D0s. o Question: are differences due to impurities in one or both sources?

8. 8 Milind V. Purohit, Univ. of South Carolina Kaon from MC Truth vs. MC as Data

9. 9 Milind V. Purohit, Univ. of South Carolina

10. 10 Milind V. Purohit, Univ. of South Carolina  2 Contribution Due to the Effect of Different Distributions with Bins Selector   (unadjusted)   (bin dist.)   /ndof   adjusted) K - Very Loose172.82.61.45 K - Loose144.74.91.20 K - Tight137.76.81.12 K - Very Tight131.46.91.06 K + Very Loose209.42.01.77 K + Loose191.03.91.60 K + Tight164.15.51.36 K + Very Tight176.95.91.46

11. 11 Milind V. Purohit, Univ. of South Carolina Charge Asymmetry

12. 12 Milind V. Purohit, Univ. of South Carolina Tracking Efficiency versus Decay Distance – Kaon Decay Mode Decay ModeBranching Fraction    63.43% -- 21.13%  5.576%   e - e 4.87%    -  3.27%  1.73%

13. 13 Milind V. Purohit, Univ. of South Carolina PID Efficiency as a function of decay distance for kaon decay mode

14. 14 Milind V. Purohit, Univ. of South Carolina Kaons Interact with the Detector

15. 15 Milind V. Purohit, Univ. of South Carolina Summary  Different methods to estimate kaon systematics have been surveyed  New approaches to estimate kaon systematics are being undertaken  As questions get answered, new questions are being raised  We need input on what is needed and feedback on whether we are headed in the right direction o Should we extend kaon studies to other particles? o Manpower is needed to do an exhaustive study of PID systematics  Analysts are encouraged to volunteer their efforts. We can learn from their experiences with PID.