Bs2JPsiGamma Workbook

Strategy Update - 16 Dec 2014

  • Adjust preselection DIRA cut to 0.9999 (preselection now ~85% signal efficient). Previously it was 0.99999 (and around 68-70% efficient)
  • With the new preselection applied run on the KstGamma MC and Data
  • Fit the LL and DD Kstar MC component separately and freeze the parameters
  • Then do data fit with exponential background component and LL, DD fraction in signal floating (this gives frac of LL = 0.47 +/- 0.17)
  • Now look at just preselected Jpsi Gamma data and compare amount of data in signal window for the LL and DD
  • This gives an estimate for the expected number of background events for LL and DD
  • Using the fraction from KstarGamma above with the back of envelope theory prediction (for total 40 signal events). Summarise all the information as:

VALUES FOR OPTIMAL BDT CUT

===== SUMMARY ====
LL:
nSig = 12.2556
nBkg = 1668.12
DD:
nSig = 13.7444
nBkg = 5773
======================
fLL Sig: 0.47137
fll Bkg: 0.224176
======================

-- MatthewKenzie - 28 Jul 2014

Topic attachments
I Attachment History Action Size Date Who Comment
Texttxt JpsiGamma.py.txt r1 manage 5.6 K 2014-08-07 - 11:08 MatthewKenzie  
Texttxt JpsiGamma_MC.py.txt r1 manage 6.9 K 2014-08-07 - 11:08 MatthewKenzie  
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Topic revision: r3 - 2014-12-16 - MatthewKenzie
 
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