1 Barbara Sciascia – LNF Barbara Sciascia Barbara Sciascia

KKl3l3 decays analysis: decays analysis:

tracking efficiencytracking efficiency

KPM meetingKPM meeting9 February 2007 - LNF9 February 2007 - LNF

2 Barbara Sciascia – LNF

Outline

• Summary of the already used tracking

• New method

• Preliminary efficiency result using the new method

• Future plans

Just a remind:

BR(Kl3) =N(Kl3) 1 1 1 (TAG(i) BR(i))

NTAG (1-fNI) FV SELE TAG(Kl3)

CF

(TRK)DATA TCA)DATA 1)DATA 2)DATA

(TRK) MC TCA) MC 1) MC 2) MC

SELE= SELE_MC

3 Barbara Sciascia – LNF

Samples purity for old samples

• Many samples can be defined: 0 - At least 1 0: only (VTX, K) parameterization. 1 - K2: also pLAB dependence, high momenta. 2 - Kl3: p* dependence. 3 - K’: also pLAB dependence, low momenta.

• Use sample 2 to correct the efficiency with a (VTX,K,p*) parameterization.• Estimate the systematic error of the correction from the comparison between the 2 and 1+3 samples.

Sample Kl3 K2 K’

1 (K2) 15 % 85 % -

2 (Kl3) 60 % 40 % -

3 (K’) 10 % 15 % 75 %

4 Barbara Sciascia – LNF

New method

Goal of the new method: increase purity sample momentum estimateFit (MINUIT)*: Starting sample: neutral vertex (NV) output + a “charged” cluster Impose “Ke3 constraints” building a 2 like variable. Obtain lepton momentum components and photon energies (5 parameters)

* Laborious work (2 weeks) to run MINUTI on queues. Many thanks to F.Fortugno and P.Santangelo

Ke3 K3 K2

Sample Ke3 K3 K2 K’

Old 60 % 40 % -

New 50 % 26 % 21 % 2%

5 Barbara Sciascia – LNF

New method: details

2 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 - m0, 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 Barbara Sciascia – LNF

Momentum resolution: components

Px

Py

Pz

Fit-true: centered around 0 35-40 MeV wide

7 Barbara Sciascia – LNF

Momentum resolutionKe3 K3 K2

• Momentum dependency of the correction: negligible for Ke3 and K3, present for K2.• Low contamination of K2 events at low momentum where the expected correction is larger. • Apply a mean correction shifted by 13 MeV.

Ke3 K3 K2

• Fit-kine -13MeV• 30 MeV resolution

8 Barbara Sciascia – LNF

Momentum resolution: Kl3 zoom

Ke3 K3

9 Barbara Sciascia – LNF

Efficiency: data

Momentum distribution (30 MeV/bin) in each (VTX,K) bin (15 bins)

10 Barbara Sciascia – LNF

Efficiency: MC

11 Barbara Sciascia – LNF

Efficiency: Data/MC correction

12 Barbara Sciascia – LNF

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 -masshypothesis

• Still missing: fit shape systematic with Ke3 AND K3

13 Barbara Sciascia – LNF

Running MINUIT on queues

Problem in managing the bothering MINUIT output messages: too large output files.In the fortran code define:LUNO = 231Open (LUNO,file=“/dev/null”)Call MNINIT(5,LUNO,7)

Add to the job file the line:# @ input = nullawhere 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