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Analyses of K S decays - status update C. Gatti, M. Palutan, T. Spadaro

Analyses of K S decays - status update C. Gatti, M. Palutan, T. Spadaro. Neutral kaon meeting, 28/4/04. Analysis status before MC massive production. raw ratio p + p - /p 0 p 0. E CRA  200 MeV 0.17  b * 0.28. 2002 data. 2001 data. R pp. published result  1 s. 2001 data.

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Analyses of K S decays - status update C. Gatti, M. Palutan, T. Spadaro

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  1. Analyses of KS decays - status update C. Gatti, M. Palutan, T. Spadaro Neutral kaon meeting, 28/4/04

  2. Analysis status before MC massive production raw ratio p+p-/p0p0 ECRA 200 MeV 0.17b*0.28 2002 data 2001 data Rpp published result  1 s 2001 data 2002 data

  3. Selection efficiencies w interference between hemispheres Old analysis: KS sel. efficiencies on MC, for events with a KL decay/interaction into EmC New analysis: KS selection efficiencies on MC, for events with a KL crash: KL interactions/decay in EmC + KL late decays p+p- sel. eff. Kcrash ECRA > 100 MeV Ke3Km3p0p0p0 p+p-p0regnucl rT (cm) rT (cm)

  4. Tracking efficiencies w interference between hemispheres Old analysis: - etrk(p) from data, 2tag vs 1tag, Kcrash@ 100 MeV (some track interference included) New analysis: - etrk(p) from data, 2tag vs 1tag, Kcrash @ 300 MeV (track interference minimized) - etrk(p) from MC, KS p+p-events, KL into EmC (no track interference here) - etrk(p) from MC, as in data (new Kcrash simul.) - etrk(e) from rad. bhabhas as in old analysis, new dedicated MC sample w a run-by-run basis - etrk(e) from radiative bhabha events either from data and from MC (no track interference here...) Difference between single-track efficiency plateau values from the 2 approaches for pions is eold(data/mc) - enew(data/mc)  -0.3% e(p+p-) = e (2 tracks from IP to EmC)  “etrk(data/MC)2” OLD NO interference Single-trk interference NEW Interference No interference Summary:

  5. esele vs t0 unbiased efficiencies given Kcrash @ 100 MeV All events KS  p0p0 Ke3Km3 p0p0p0 p+p-p0 regnucl rT (cm) rT (cm) KS-t0 acci-t0 rT (cm) rT (cm) KS p+p- rT (cm) rT (cm)

  6. Selection efficiencies vs t0 : old analysis scheme Npemeas = Npe e(KL far) epe (trks | KL far) epe(t0 | tracks)  e(crash | t0pe) N+-meas = N+- e(KL far) e+-(trks | KL far) e+-(t0 | tracks) e(crash | t0+-) N00meas = N00 e(KL far) e00(prompt clus | KL far)  e(crash | t000) Ratios of tagging efficiencies from data, but systematic error is a delicate task ( ecra+-/ecra00 ecra+-/ecra+-reph ) Can take precisely e(t0 | p track) from data Don’t need to rely on MC for p cluster efficiencies ab-initio Problems: 1) MC KL-far sample does not completely represent data 2) Wider b window  Higher prob. for Kcr w global t0 from accidental: +-, pe Modified distribution of accepted KL’s, sel. eff. bias 00 Direct loss on selection efficiency 0.3% effect Need a better formula

  7. Selection efficiencies: complete treatment Can exploit new simulation of KL interactions, reliability of b distribution Label KCRMC an event with a crash using the value of MC-true t0 n Nimeas = Ni e(KCRMC) ei (sel & t0 | KCRMC )  Tn Pn (crash | KCRMC) dn + + Ni e(KCRMC) ei (sel & t0 | KCRMC )  Tn Pn (crash | KCRMC) dn  n where: Tn is the t0 population for n-bunch, from data (KS tag, KL p+p-p0 id) Pn is the probability for a KCRMC event to remain identified, given an n-bunch shift, from data Pn is the probability for a not-KCRMC event to be identified, thanks to an n-bunch shift, from data  In absence of hemisphere interference and acc., this formula reduces to the old one. The second term is a correction of order 10-3 wrt to the first term

  8. Conclusions • New method to chain t0 & tag & selection efficiencies • - no substantial modifications, tools for extraction of various terms are ready • - have to check consistency: use MC-estimated efficiencies (selection, t0 population) and apply the method on MC events • Currently running on data and new MC production: • - tracking efficiencies (Bhabha + KSKL) • - cluster efficiencies ( f  p+p-p0 )

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