Systematic errors associated with PID

Milind V. Purohit

BaBar Analysis Tools Workshop October, 2005

2Milind 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.

3Milind 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 comparisonso Using PID weight statisticso Using PID killing vs no killingo Other methodso Unclear or no explanation

4Milind V. Purohit, Univ. of South Carolina

Summary of Some PID Systematic Error DeterminationsAvailable at http://www.slac.stanford.edu/~purohit/internal/PidSyst.htmle (%) mu (%) pi (%) K (%) p (%) BAD #s Method, Notes

      0.5   1213, 824, 1213 Data vs. MC: Control Sample vs. PID group efficiencies

      5   1205, 971 Data - MC comparison. Depends on D0 mass cut.

1.3         1259, 1184 Data - MC comparison.

  3.5       967, 1076 Data - MC comparison.

      1   1077, 664 Data - MC comparison.

    0.5 0.5   1137, 824 Data - MC comparison.

0.7 - 3.8 1075, 938 PID weight statistics.

    0.49 0.75   1179, 825 PID weight statistics.

2.2 2.2       1147, 323 PID weight statistics.

      3.0   1159, 1013 PID weight statistics.

2 3   2   1255, 1214 PID weighting. Using PID weight statistics?

5 5   5   1187, 542 PID killing vs. No PID killing.

1.0 2.0 0.7     1105, 1032 PID killing vs. No PID killing.

        3.5 1107, 1071 PID killing vs. No PID killing.

      3.0   1129, 1044 PID killing vs. No PID killing.

      1   1239, 1088 PID effect cancels, but accounts for various algorithms.

      1.1   1243, 818Giampiero Mancinelli's study. See http://www.slac.stanford.edu/BFROOT/www/Organization/CollabMtgs/2001/detDec2001/Wed1a/giampi.pdf

2 2   2   658 "Based on data-MC comparisons."

0.5         1077, 664 Entire hadronic Mis-ID rate.

      3   1135, 94 Different running periods.

      5.2   1271, 1203 Unclear.

      1.0   1027, 768 Unclear.

      2.0   1037, 902 "Common BaBar practice."

0.2 - 2.0 0.2 - 2.0       1225, 1146 Unclear.

2.6         1243, 818 No explanation.

      2   1073, 697 "[As in] similar analyses."

      20   1077, 664 Arbitrary.

2.0 3.0       1161, 1091 No explanation.

      1   1107, 1071 No explanation.

5Milind 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

6Milind 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!

7Milind 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?

8Milind V. Purohit, Univ. of South Carolina

Kaon from MC Truth vs. MC as Data

9Milind V. Purohit, Univ. of South Carolina

10Milind V. Purohit, Univ. of South Carolina

2 Contribution Due to the Effect of Different Distributions with Bins

Selector (unadjusted) (bin dist.) /ndofadjusted)

K- Very Loose 172.8 2.6 1.45

K- Loose 144.7 4.9 1.20

K- Tight 137.7 6.8 1.12

K- Very Tight 131.4 6.9 1.06

K+ Very Loose 209.4 2.0 1.77

K+ Loose 191.0 3.9 1.60

K+ Tight 164.1 5.5 1.36

K+ Very Tight 176.9 5.9 1.46

11Milind V. Purohit, Univ. of South Carolina

Charge Asymmetry

12Milind V. Purohit, Univ. of South Carolina

Tracking Efficiency versus Decay Distance – Kaon Decay Mode

Decay Mode Branching Fraction

63.43%

- 21.13%

5.576%

e-e 4.87%

- 3.27%

1.73%

)()(

)(

unmatchedNmatchedN

matchedNeff

truthtruth

truthtracking

13Milind V. Purohit, Univ. of South Carolina

PID Efficiency as a function of decay distance for kaon decay mode

14Milind V. Purohit, Univ. of South Carolina

Kaons Interact with the Detector

15Milind 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 directiono 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.