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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

OversmearingTunfold Errors

Michael Gardner

Dilepton – Z Group

May 26th, 2014


Reminder
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
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.


Oversmearing
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).


Results
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:


Results for all
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
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
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}


Summary of z values
Summary of Z values

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


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