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Barbara Sciascia

K  l3 decays analysis: tracking efficiency. KPM meeting 9 February 2007 - LNF. Barbara Sciascia. Outline. Summary of the already used tracking New method Preliminary efficiency result using the new method Future plans. Just a remind:. N(Kl3) 1 1 1  ( e TAG (i) BR(i) ). a CF.

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Barbara Sciascia

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  1. Kl3 decays analysis: tracking efficiency KPM meeting 9 February 2007 - LNF Barbara Sciascia

  2. Outline • Summary of the already used tracking • New method • Preliminary efficiency result using the new method • Future plans Just a remind: N(Kl3) 111 (eTAG(i) BR(i)) aCF BR(Kl3) = NTAG(1-fNI)eFVeSELEeTAG(Kl3) e(TRK)DATAe(TCA)DATA e(g1)DATAe(g2)DATA   eSELE= eSELE_MC e(TRK) MCe(TCA) MC e(g1) MCe(g2) MC

  3. Samples purity for old samples • Many samples can be defined: • 0 - At least 1 p0: only (rVTX, qK) parameterization. • 1 - Kp2: also pLAB dependence, high momenta. • 2 - Kl3: pp* dependence. • 3 - Kt’: also pLAB dependence, low momenta. • Use sample 2 to correct the efficiency with a (rVTX,qK,pp*) parameterization. • Estimate the systematic error of the correction from the comparison between the 2 and 1+3 samples.

  4. New method * Laborious work (2 weeks) to run MINUTI on queues. Many thanks to F.Fortugno and P.Santangelo Goal of the new method: increase purity sample momentum estimate Fit (MINUIT)*: Starting sample: neutral vertex (NV) output + a “charged” cluster Impose “Ke3 constraints” building a c2 like variable. Obtain lepton momentum components and photon energies (5 parameters) Ke3 Km3 Kp2

  5. New method: details c2 contributions (electron mass hypothesis): 1/2- D(dt): ToF difference between photon and “lepton” (mass hypothesis needed) 3 - EMISS-PMISS at kaon decay vertex (mass hyp.) 4 - ECLU/ELEPT, using charged cluster (mass hyp.) 5 - dMIN, between track extrapolation and charged cluster position. 6 - mp0, photons invariant mass 7/8 - Energy of photon clusters. 9 - Kaon+lepton ToF (mass hyp.) Input resolutions from NV: 7 MeV for each PK component 5 cm for x and y vertex position 7 cm for z vertex position Fitting also with a different mass hypothesis (muon) should improve the “god” sample (to be implemented)

  6. Momentum resolution: components Px Py Fit-true: centered around 0 35-40 MeV wide Pz

  7. Momentum resolution Ke3 Km3 Kp2 • Fit-kine  -13MeV • 30 MeV resolution • Momentum dependency of the correction: negligible for Ke3 and Km3, present for Kp2. • Low contamination of Kp2 events at low momentum where the expected correction is larger. • Apply a mean correction shifted by 13 MeV. Ke3 Km3 Kp2

  8. Momentum resolution: Kl3 zoom Ke3 Km3

  9. Efficiency: data Momentum distribution (30 MeV/bin) in each (rVTX,qK) bin (15 bins)

  10. Efficiency: MC

  11. Efficiency: Data/MC correction

  12. Conclusions and future plans • New method to measure tracking efficiency correction: • Kl3 higher purity (75% instead of 60%) • momentum knowledge (35 MeV resolution) • Efficiency on Data and MC: running on queues • Use *NEW* correction to determine BR’s • Estimate systematic error of the new method • New sample may have a too big statistical error • Implement also the FIT using the m-masshypothesis • Still missing: fit shape systematic with Ke3 AND Km3

  13. Running MINUIT on queues Problem in managing the bothering MINUIT output messages: too large output files. In the fortran code define: LUNO = 231 Open (LUNO,file=“/dev/null”) Call MNINIT(5,LUNO,7) Add to the job file the line: # @ input = nulla where nulla is any file, also empty. At running time define the environment variables: setenv XLFRTEOPTS “unit_vars=yes” setenv XLFUNIT_231 “/dev/null” * Laborious work (2 weeks) to run MINUTI on queues. Many thanks to F.Fortugno and P.Santangelo

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