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SS kaon tagger + OS taggers

SS kaon tagger + OS taggers. Míriam Calvo, Marco Musy 12th March 2009. Nnet approach. Tagging B s J/. 1st step: calibrate p0, p1 2nd step: Use the probability per event to split into 5 categories Or , use a probability per event including same side.

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SS kaon tagger + OS taggers

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  1. SS kaontagger + OS taggers Míriam Calvo, Marco Musy 12th March 2009

  2. Nnetapproach Tagging BsJ/ • 1st step: calibrate p0, p1 • 2nd step: Use the probability per event to split into 5 categories • Or, use a probability per event including same side i = p0 + p1 ii = SS k , , e, OS k, Qvtx OS from B+J/ K+(directly) SS from BsDs (oscillation) OSof each category from B0J/ K* (oscillation) effincreasesfrom 2.2% to 3.4% using 5 categories effincreasesto 4.5% including SS k (6.4% splittinginto 5 categories)

  3. Combining SS with OS taggers • Howtoproceed? • Combine SS kaontogetanevent-per-eventprobability of mistagusingthealreadysortedtaggingcategoriesforthe OS taggers. • Someconditions are needed: • SS kaon vs Nnet output isthesameamong Bs decays. • SS and OS taggersmistag are notcorrelated. • Triggereffectalsoneedtobestudied.

  4. SS kaon vs NNET output SS K Fit p0+p1*x p0=(7±9)·10-3 p1=0.97±0.03 p0=(-1.0±0.7)·10-2 p1=1.00±0.02 p0=(0.8±1.5)·10-2 p1=0.95±0.04 BsJ/  (98k sel) BsDs (133k sel) BsDs (27k sel)  Compatible – same SSK (Nnet output) dependenceforall Bs decays BsDscan beusedtocalibrateNnet output from data BsDshigherannualyield, butpoorproper time resolution (use doubletaggingmethod)

  5. BsJ/ Taggercorrelations Correlationmatrix (Taggerdecision): ------------------------------------------------  e OS k SS k Qvtx : +1.00 e: -0.01 +1.00 OS k: +0.03 +0.03 +1.00 SS k: +0.02 +0.01 +0.03 +1.00 Qvtx: +0.10 +0.07 +0.19 +0.02 +1.00 ------------------------------------------------ Higher correlations between Q_vrtx and OS muon &kaon taggers but not with Same Side

  6. OS taggers SS kaon true true Offset due to OS correlations Mistag is under-estimated (product of individual taggers probabilities) 1- p 1- p + Correlation Qvtx and other OS taggers is ~ 10-20% true OS+SS 1- p

  7. Reminder • OS taggers () calibrated with B+J/K+. • SS kaon () calibrated with BsDs. • Get rid of OS correlations by measuring OS in 5 categories (Nnet approach) in a control sample as B0J/K*. OS taggers • Bd2JpsiK* •  (cat1) = 44.2±0.3 •  (cat2) = 36.3±0.5 •  (cat3) = 29.0±0.6 •  (cat4) = 23.4±0.7 • (cat5) = 18.2±0.5 • (combined) • OS = 36.25 ± 0.24% • tag = 44.97 ± 0.16% • eff = 3.40 ± 0.11% 1 2 3 4 5 Prob

  8. OS taggers SS kaon true true OS taggers Taking measured  In 5 categories (to get rid of correlations) 1- p 1- p + true Correlation OS taggers – SS kaon is 2.2% OS+SS 1- p

  9. Getting a probability per eventfrom OS+SS OS+ SameSideprobability: • Bd2JpsiK* •  (cat1) = 44.2±0.3 •  (cat2) = 36.3±0.5 •  (cat3) = 29.0±0.6 •  (cat4) = 23.4±0.7 • (cat5) = 18.2±0.5 p i, OS Probability per eventforBs2JpsiPhi eff(OS) = 3.40% • (average) • = 35.77 ± 0.21% • tag = 55.54 ± 0.16% • eff = 4.50 ± 0.13% Splitting into 5 categories 1 2 3 4 5 • (combined) • = 33.19 ± 0.21% • tag = 55.54 ± 0.16% • eff = 6.28 ± 0.15% p

  10. Probability per eventwithoutQvtx • OS (combined) • = 31.1 ± 0.3% • tag = 20.48 ± 0.13% • eff = 2.93 ± 0.10% (< 3.40%) true OS taggers alone (without Qvtx) • OS + SS (average) • = 32.10 ± 0.25% • tag = 38.18 ± 0.16% • eff = 4.89 ± 0.14% (> 4.50%) • OS + SS (combined) • = 30.10 ± 0.25% • tag = 38.18 ± 0.16% • eff = 6.05 ± 0.14% (< 6.41%) 1- p No bias due to correlations Approximately same effective efficiency without Qvtx (work on-going to improve this tagger)

  11. BsJ/ intoCategories… withQvtx withoutQvtx

  12. L0 TISTOSTOBBING Phase space corrections…

  13.  vs Nnet  Before L0 Fit p0+p1*x p0=(7±9)·10-3 p1=0.97±0.03 p0=(-1.0±0.7)·10-2 p1=1.00±0.02 p0=(0.8±1.5)·10-2 p1=0.95±0.04 SS K BsJ/  (98k sel) BsDs (133k sel) BsDs (27k sel) Samedependencebefore/after L0. Nnet output After L0 SS K BsJ/  (92k sel) BsDs (63k sel) BsDs (24k sel) Fit p0+p1*x p0=(10±9)·10-3 p1=0.96±0.03 p0=(-8±8)·10-4 p1=0.95±0.01 p0=(4.3±1.6)·10-3 p1=0.92±0.05 Nnet output

  14.  vs Bs pt Fit p0+p1*x p0=0.408±0.006 p1=(-7.4±0.7)·10-3 p0=0.417±0.008 p1=(-9.1±0.7)·10-3 p0=0.416±0.013 p1=(-9.2±1.1)·10-3 After L0 SS K BsJ/  BsDs BsDs • Differences in phasespacecan introduce differences in  of a givencategoryamongdecays. • Corrections can beperformed once knownthe(Bs pt) dependence (obtainedfrom control channels). rec’ted Bs pt Fit p0+p1*x p0=0.337±0.007 p1=(1.8±0.9)·10-3 p0=0.30±0.01 p1=(3.4±1.0)·10-3 p0=0.340±0.016 p1=(1.9±1.6)·10-3 OS K rec’ted Bs pt

  15. Corrections might be needed when splitting into 5 categories (Nnet approach) Bs pt  vs Bs pt Cat 1 Cat 2 Cat 1 Cat 2 Cat 3 Cat 4 Cat 3 Cat 4 Cat 5 Cat 5 BsJ/  BsDs BsDs OS + SS taggers after L0

  16. (BsJ/ ) OS taggers SS tagger  vs Bs pt cat1 cat2 cat1 cat2 cat3 cat4 cat3 cat4 cat5 cat5 all taggers 1/6 of eventsinclude SS kaon Nearly flat! • (OS+SS) of eachcategory compatible for • BsJ/  andBsDs

  17. PID approach Correctionsneededalsofor: • OS, e, k, Qvtx from B+J/ K+(directly) B0J/ K* (from oscillation) • SSkfrom BsDs  (double tagging) BsDs (from oscillation) • 41 possible combinations, sort into 5 categories according to its : eff ~ 4.9%  Cat 4 Cat 3 Cat 2 Cat 5 Cat 1 PID combination

  18. After L0 BsDs Nnet: BsJ/ Nnet: (BsDs numbers in the backup)

  19. TisTosTobbing L0 After L0 Before L0 Bs pt (GeV) (TIS includes TIS+TOS) L0 TOS L0 TIS Bs pt (GeV) (TIS: remaining differences due to offline selection)

  20. BsDs TIS (55.1%) TOS (44.2%) Nnet: BsJ/ TIS (23.5%) TOS (74.3%) Nnet:

  21. Conclusions Same Side Kaon can be used in combination with Opposite Side taggers to get an event-per-event probability of mistag. • From Nnet output calibrated with control channels. • eff~6.3% (3.4% without SS) Qvtx main source of OS tagger correlations. • Get rid of correlations by measuring global OS wrong tag fraction from control channel (5 categories). • Actually without it eff~6%. • Needs to be studied further. Trigger effects matters to get a ‘global’ , but not as event-per-event weight.

  22. BackUp

  23. BsDs BsJ/

  24. Bs2JpsiPhi Bs2DsPi Bs2DsMuNu OS kaon SS kaon p0=(40.6±0.8)·10-2 p1=(-7.7±0.8)·10-3 p0=(40.8±1.4)·10-2 p1=(-9.7±1.2)·10-3 p0= (39.0±1.7)·10-2 p1=(-7.4±1.5)·10-3  p0=(34.3±0.9)·10-2 p1=(1.6±1.1)·10-3 p0=(31.9±2.0)·10-2 p1=(2.6±1.8)·10-3 p0= (33.7±2.3)·10-2 p1=(2.6±2.2)·10-3  TOS TOS B pt B pt p0=(31.0±1.4)·10-2 p1=(2.9±1.4)·10-3 p0=(29.3±1.2)·10-2 p1=(3.1±1.1)·10-3 p0= (33.3±2.4)·10-2 p1=(1.8±2.2)·10-3  TIS  p0=(42.7±1.2)·10-2 p1=(-8.2±1.2)·10-3 p0=(42.9±1.0)·10-2 p1=(-9.4±0.9)·10-3 p0= (43.9±1.9)·10-2 p1=(-11.0±1.7)·10-3 TIS B pt B pt Not expected to be different between TIS and TOS (just a check)

  25. SS kaon tagger  BsJ/  BsDs BsDs Fit p0+p1*x p0=(0.9±1.1)·10-2 p1=0.96±0.03 p0=(-1.8±1.3)·10-2 p1=0.98±0.04 p0= (0.8±2.0)·10-2 p1=0.90±0.06 TOS NNet  TIS Fit p0+p1*x p0=(-0.1±1.7)·10-2 p1=0.98±0.06 p0=(-0.6±1.2)·10-2 p1=0.97±0.03 p0= (0.3±2.4)·10-2 p1=0.91±0.07 NNet Not expected to be different between TIS and TOS (just a check)

  26. BsDs Nnet: Double tagging method: F = probability that taggers agree = Nagree/NDT

  27. BsDs momentumcorrection (from S. Poss)

  28. SameSideKaon Neural net output calibration From BsDs control channel

  29. BsDs offline selection Jeremie Borel / Vladimir Gligorov (DaVinci v19r12 / v22r0p2) Yield ~ 146/183k (after L0) DC06 selected 113/134k (~39/37k with SSK tagger); 50/63k after L0 Backgrounds: B/S~0.34/0.41 _ BsDs  BsDs X BdD  bDs p bc  Signal Bkg 29 m (GeV/c2) m (GeV/c2)

  30. PDFsused in ROOFIT SIGNAL: BACKGROUND: sig RooBDecay mixState= unmixedifcharge() = tagger_decision mixedifopposite (bkg=0.5) RooDecay acceptance: s, s, ms fixed

  31. Fitresults Floating Parameter FinalValue +/- Error -------------------- -------------------------- B/S 3.2075e-01 +/- 2.93e-03 a 7.1132e+00 +/- 1.90e-01 bkg_S1 1.8975e-02 +/- 6.73e-02 bkg_m1 -1.9603e-01 +/- 2.44e-01 bkg_mexp -3.7567e-01 +/- 1.37e-01 bkg_tau 6.7124e-01 +/- 5.08e-03 fr1 3.4824e-01 +/- 6.33e-03 mean 3.8342e-03 +/- 7.58e-04 omegaB 5.1069e-01 +/- 3.23e-03 par0 -2.1409e-03 +/- 5.11e-03 par1 9.6138e-01 +/- 1.31e-02 sig_mass_mB 5.3683e+00 +/- 5.82e-05 sig_mass_sB 1.1764e-02 +/- 1.00e-04 sig_mass_sB2 1.9794e-02 +/- 1.58e-04 sigma 4.3791e-02 +/- 8.62e-04 SS k Fit: p0+p1*x p0 = (-6±7)·10-3 p1 = 0.99±0.02 RooFit p0 =(-2±5)·10-3 p1 =0.96 ±0.01 Nnet

  32. Offline Selectioncomparison BsDs

  33. 3

  34. Jeremie/ Vava m = ± 50 / 50MeV (HLT eff missing) +25% B/S ~ 0.34/ 0.43 (Increase of bkg because loose PID cuts for the bachelor pion) +25%

  35. BsDs offline selected events Vava Jeremie B mass B p B pt B 

  36. Bkg Bs mass Bs2DsX Bd2DPi Lb2LcPi LbDsP Vava Jeremie bb-bar

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