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Preliminary results for the BR(K S  gg)

Preliminary results for the BR(K S  gg). M. Martini and S. Miscetti. Talk Layout. Short summary of strategy for the measurement DATA-MC QCAL calibration Signal/background fit repeated in different conditions: - with cos( q ) - without cos( q )

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Preliminary results for the BR(K S  gg)

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  1. Preliminary results for the BR(KSgg) M. Martini and S. Miscetti

  2. Talk Layout • Short summary of strategy for the measurement • DATA-MC QCAL calibration • Signal/background fit repeated in different conditions: • - with cos(q) • - without cos(q) • Determination of efficiency for the signal • Study of normalization sample • Preliminary estimate of BR • First look at background shapes • Plans/prospects

  3. Data sample and preselection • We have analized 1.6 fb-1 of DATA (2001-2002-2004 • and part of 2005 sample). 400 pb-1 still missing on 2005. • whole production of neukaon MC 2001-2002 used for the • bkg (450 pb-1 ) • ksr04 used for the signal • Started using the preliminary sample 100 pb-1 of the 2004 MC. • Not yet for shapes .. Checking rates only. • From NA48 results: BR(KSgg) = 2.78x10-6, we expect to have • tagged 1550 signal events with Kcrash. • Kcrash events • Preselection: consists of requiring 2 “and only 2” prompt photons • with Eg >7 MeV, cos(q) < 0.95 and T<min(5s, 2 ns) • Qcal veto

  4. Example fit 2d chi2 ... DATA BKG • KS tagged from Kcrash • (122x106 events) • 2 prompt photons required • (496000 events) • The major background is • constituted by KS2p0 with • 2 lost photons. • To disentangle signal from • background we use: • Kinematic fit (c2<20) • We then look at the scatter plot Mgg vs qgg, where: • - qgg Opening angle between the two photons in the KS cms • - Mgg Reconstructed gg mass

  5. Example fit 2d chi2 ... DATA SIG • KS tagged from Kcrash • (122x106 events) • 2 prompt photons required • (496000 events) • The major background is • constituted by KS2p0 with • 2 lost photons. • To disentangle signal from • background we use: • Kinematic fit (c2<20) • We then look at the scatter plot Mgg vs qgg, where: • - qgg Opening angle between the two photons in the KS cms • - Mgg Reconstructed gg mass

  6. Data vs MC: QCAL rates (2001-2002) • After splash filter • events with 3, 4, 5 g • prescaled of 400 • QCAL (-5 < DTqcal < 5) ns DT=Tqcal-Rqcal/c (ns)

  7. Data vs MC: QCAL rates (2004-2005) • After splash filter • events with 3, 4, 5 g • prescaled of 400 • QCAL (5 < DTqcal < 35) ns DT=Tqcal-Rqcal/c (ns)

  8. Extraction of losses... • We defined two windows, early and late in DT: • - Early: (-50 ; -40) ns • - Late: (70 ; 80) ns Ploss in window After splash filter TOT events Ng=2,3,4 weighted mean Event in window

  9. Extraction of losses... • For the moment we have only used the early window (we can use • late fraction as systematic) • since we have difference between 2001-2002 and 2004-2005 sample, • we calculate different values of Ploss: • 2001-2002 Ploss = (4.85  0.07)% • 2004-2005 Ploss = (15.7  0.07)% • - eQCAL is evaluated as: • eQCAL(DATA) = 1 - Ploss

  10. QCAL data/MC efficiency • For each period all numbers with Ng=2,3,4 fit with the following • stuff ... • - we calculate the ratio: • We found compatible value of R for the different DATA sample Qcal vetoed MC Qcal vetoed DATA

  11. QCAL data/MC efficiency results Using the results on Ploss for the different DATA sample, we can extract the QCAL efficiency:

  12. MC checks for QCAL efficiency • QCAL rejected events: • ALL events • Cosq accepted events • Cosq rejected events • Energy of accepted events

  13. MC checks for QCAL efficiency • QCAL survived events: • ALL events • Cosq accepted events • Cosq rejected events • Energy of accepted events

  14. MC checks for QCAL efficiency cosq>0 ; cosq<0 We still have events impinging the QCAL that survived QCAL cut. We can improve the simulation studying these events.

  15. Signal-background fit 2001-2002 sample weights from fit •• DATA -- MC all Signal Background

  16. Signal-background fit 2004-2005 sample weights from fit •• DATA -- MC all Signal Background

  17. Fit using costheta (preliminary) 2001-2002 sample: comparison without and with cosq in the fit cosq cosq

  18. Fit using costheta (preliminary) 2004-2005 sample: comparison without and with cosq in the fit cosq cosq

  19. Fit using costheta (preliminary) 2001-2002 sample: comparison without and with cosq in the fit cosq

  20. Fit using costheta (preliminary) 2004-2005 sample: comparison without and with cosq in the fit cosq

  21. Fit results and analysis efficiency The analysis efficiency (c2 cut after kcrash and acceptance selection) is the same for the two samples since up to now we have used MC 2001-2002 only. : e(c2)=(63.30.7)%

  22. Signal Acceptance, Total Efficiency Using KSR04 MC production, we evaluate the signal efficiency requiring KL-far events and counting events with Ng=2 Using the standard efficiency curves, we obtain: e(ACC)(Ng=2) = 83.2  0.2stat (1) The systematic error has been evaluated varying the cone (0.6, 0.7, 0.8) and using the maximum variation from (1): e(ACC)(Ng=2) = 83.2  0.2stat 0.1sys etot (kcrash given) = e(acc) * e(qcal) * e(ana) For the moment statistics and systematics together.

  23. Normalization sample Kcrash with Ng=4, Ng=3,4,5 prescaled of 400. Splash filter applied. Stability plot shown with Ng = 4

  24. Kcrash counter stability (2001-2002) 2001 2002

  25. Kcrash counter stability (2004-2005) 2004 2005

  26. KS2p0 efficiency Using a sample of 160 Kevents, extracted from 2001 and 2002 statistics, we calculate KS2p0 efficiency using events with a KLfar definition: e(Ng=2) = ( 65.0  0.02stat )% Using the same method applied for the signal, we can evaluate a first systematics on this parameter: e(Ng=2) = ( 65.0  0.2stat 0.1sys )%

  27. Kcrash Final normalization Using KS2p0 efficiency, we can extract the number of Kcrash of the normalization sample Total number of Kcrash: 159.8 x 106 We can compare this results with Ncrash obtained integrating Ng = 3, 4, 5: Ncrash(3, 4, 5) = 159.5 x 106

  28. First BR estimate eTOT(2001-2002) = (50.1 ± 0.6)% Ncrash = 34.7 x 106BR(KS2p0) = (31.05 ± 0.14)% BR(KSgg) = 2.57 x 106 eTOT(2004-2005) = (44.4 ± 0.5)% Ncrash = 125.1 x 106 BR(KSgg) = 2.59 x 106 Combined result: BR(KSgg) = (2.58 ± 0.17)x 106

  29. Fast simulation of Background • To study the fit uncertainty as a function of MC statistics we have developed a method based on “hit or miss”. • The procedure is only based on MC signal and background. • Recipe: • use the original 2d-distribution from sig and bkg, to create 2 • smoothed distribution • Use hit or miss to create N different distribution for signal and • background for different MC statistics • create a fake data distribution using sig and bkg from hit or miss • with entries from fit • repeat the fit procedure N times for each statical point.

  30. Fast simulation of Background Metti qualche plot di preparazione per hit or miss

  31. Hit or miss Signal and bkg statistical error as a function of the used MC statistics Actual stat: Mcfact=1 Using twice MC statistics we can lower signal uncertainty of a factor 10%

  32. Hit or miss Stability of signal and bkg event as a function of MC used statistics.

  33. Look at background shapes DATA-MC comparison for bkg enriched samples with c2 > 100, ?

  34. Plans-prospects • We need to study the systematics on the spectra shapes: • 1) apply MC energy scale • 2) calibration check with background dominated samples • 3) calibration with KS2p0 • 4) effect of DATA-MC differences on QCAL efficiency • We will process the few missing pb-1 of data and the 2004-2005 • MC generated so far. • We are working on “fixing” the QCAL simulation to answer to • point 4) • Study on the tag bias • Meeting with referees • Start writing documentation and planning for a pre-xmass • blessing

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