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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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Kl3 decays analysis: tracking efficiency

KPM meeting

9 February 2007 - LNF

Barbara Sciascia

outline
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

samples purity for old samples
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.
new method
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

new method details
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)

momentum resolution components
Momentum resolution: components

Px

Py

Fit-true:

centered around 0

35-40 MeV wide

Pz

momentum resolution
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

efficiency data
Efficiency: data

Momentum distribution (30 MeV/bin) in each (rVTX,qK) bin (15 bins)

conclusions and future plans
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
running minuit on queues
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