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Oversmearing Tunfold Errors

Oversmearing Tunfold Errors. Michael Gardner Dilepton – Z Group May 26 th , 2014. 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.

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Oversmearing Tunfold Errors

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  1. OversmearingTunfold Errors Michael Gardner Dilepton – Z Group May 26th, 2014

  2. 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

  3. Kolmogorov-Smirnov Test • Results of K-S test listed here are uniform, from 0 to 1. • For Zee 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.

  4. 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).

  5. 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:

  6. 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);

  7. 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% +.

  8. 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}

  9. Summary of Z values • https://twiki.cern.ch/twiki/pub/CMS/DileptonEWK/Summary_Values.txt

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