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Oversmearing Tunfold ErrorsPowerPoint Presentation

Oversmearing Tunfold Errors

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Presentation Transcript

Reminder

- Comments on MC vs. Data: Z->ee in PbPb plot.
- ~350 Zs in ee in PbPb Data – given these stats, and the fact that yield is based on counts (not fits), want to justify not doing more calibration

Kolmogorov-Smirnov Test

- Results of K-S test listed here are uniform, from 0 to 1.
- For Zee in PbPb:
- 30 bins, K-S result: 0.239
- 60 bins, K-S result: 0.165
- UnbinnedK-S result: 0.061
- 30 bins (60-120), Χ2 p-value: 4.0348e-007 (tails).
- 30 bins (80-100), Χ2 p-value: 0.121982.

- Not Terrible: we try oversmearing MC, to see what the best fit is, and see effect on acc, eff, etc.

Oversmearing

- Idea:
- take each electron (or muon), and smear it’s pT (for each lepton, I did this 20 times):
- 1. adding a random number from a gaussian distribution of mean 0, with sigma = M GeV (modeling error in background subtraction).
- 2. multiplying by a random number from a gaussian of mean 1, with sigma = N% (modeling error in reconstruction).

- recombine electrons to form new Z.
- compare new distributions, find smearing that gives smallest Χ2 (looking 80 < Mass < 100).

- take each electron (or muon), and smear it’s pT (for each lepton, I did this 20 times):

Results

- Z ee in PbPb:
- Χ2 p-value Plot:
- Value lower than shownbefore (6%), since MCdone20x. Going forshapenot exact value.

- Adding: 1.8 (1.6) GeV shift (Χ2/ndf = 0.84); should be looking at 10 bins? 1.5 GeV
- Multiplying: 4.5% shift (Χ2/ndf = 0.90);
- No Smearing: Best Smearing:

- Χ2 p-value Plot:

Results for All

- Z ee in PbPb:
- Adding: 1.5 GeV shift (Χ2/ndf = 0.84);
- Multiplying: 4.0% shift (Χ2/ndf = 0.90);

- Z ee in pp: (MC is slightly wider)
- Adding: 0.0 GeV shift (Χ2/ndf = 1.16);
- Multiplying: 0.0% shift (Χ2/ndf = 1.16);

- Z μμ in PbPb:
- Adding: 0.7 GeV shift (Χ2/ndf = 0.94);
- Multiplying: 1.5% shift (Χ2/ndf = 0.92);

- Z μμ in pp:
- Adding: 0.6 GeV shift (Χ2/ndf = 0.67);
- Multiplying: 1.5% shift (Χ2/ndf = 0.65);

Effect of Smearing on Acc * Eff

- Most heavily seen vs. pT. In centrality there will be a small decrease in efficiency, as those Zs close to the 60 and 120 GeV boundaries may be smeared out of the range on not be counted.
- For Z ee in PbPb, with the 1.8 GeV shift:
- Overall Effect: Acc x Eff drop of 0.02%.
- Big change vs. Centrality: 0.1% -.
- vs. y: 0.8% -, 0.7% +.
- vs. pT (first 2 bins 0-5, 5-10): 13% -, 17% +.

TUnfold Errors

- Problem #1: Stat. Errors, not propogated through Tunfold.
- Z ee in PbPb:
- For TUnfold, create a Matrix of Gen vs. RecopT
- pT bins (for Tunfoldnum_rec > num_gen):
- RecopT Bins = 0;5;10;15;20;25;30;40;50;75;100;500.
- Final pT Bins = 0;5;10;20;30;40;50;100.

- Running 100k Toy MC, the Stat. Uncertainties:
- Before TUnfold:
- Num_pT[nBins] = {94,91,52,25,21,11,16,12,5,2,0}
- StatUnc % ~ {10,10,14,20,22,30,25,29,45,70,…}

- After:
- Final_num[nBins] = {100.4,90.3,64,35,14,18,8}
- Fin.StatUnc % = {14,18,17,23,44,34,40}
- What was Used = {10,10,11,18,25,29,38}

- Before TUnfold:

Summary of Z values

- https://twiki.cern.ch/twiki/pub/CMS/DileptonEWK/Summary_Values.txt

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