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Charged kaon lifetime : preliminary result

P. Massarotti. Charged kaon lifetime : preliminary result. Summary:. Events selection Reconstruction efficiency Resolution function t measurement Conclusions. t measurement at KLOE. large statistics good resolutions kaon decays on flight measurements of t + and t -.

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Charged kaon lifetime : preliminary result

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  1. P. Massarotti Charged kaon lifetime: preliminary result

  2. Summary: • Events selection • Reconstruction efficiency • Resolution function • t measurement • Conclusions

  3. t measurement at KLOE • large statistics • good resolutions • kaon decays on flight • measurements of t+andt-

  4. t: experimental picture TheParticle Data Group measures arenotin agreement t = (12.385±0.024)ns

  5. K vtx Kmn tag Li K m Strategy • Signal selection • Self triggering muon tag • K track on the signal side • Decay vertex • Signal K extrapolated to the IP. dE/dx correction applied along the path. T Li = step length Vertex efficiency and resolution functions needed wrt the proper time of the Kaon

  6. Trg eff Tag eff Why self-triggering tag?Trg and Tag (without self trg request) efficiency as a function of the charged kaon kine proper time Both the efficiencies have a slope

  7. Tag self trg Tag self trg Why self-triggering tag?Self Trg tag efficiency as a function of the charged kaon kine proper time The self trg tag efficiency is constant in the region between 10 and 35 ns

  8. Events selection and analysis background • Proper time distribution • Background analisys • Resolution functions for the background families • Background rejection • Proper time distribution after the cut

  9. Proper time distribution The main distortion of the slope comes from the badly reconstructed Kaon vertices We evaluate on MonteCarlo this effect

  10. Background analysis • Five families: • “golden”: good vertex (~ 69.1%) • kaon hits associated to daughter track (~ 20.6%) • daughter hits associated to kaon track(~ 5.2%) • early pion decay (~ 1.4%) • K± broken track(~3.7%)

  11. Daughter P* with kaon mass hypothesis Kaon broken tracks Cut at 100 MeV MeV/c Families proper time resolutions kaon hits associated to daughter track All (but one) the dist are centered within 200 ps good track K± broken track Mean: -7.6 ns daughter hits associated to kaon track early pion decay

  12. Definition of signal sample: • Cut at 100 MeV/c on P* with 94% efficiency • 73% golden and 27% other families ( worse resolution) Broken tracks: less than 1% Measurement region

  13. Ep,xp,tp Eg,tg,xg p± xK pK lK Kmn tag tm t0 pK p0 Eg,tg,xg Reconstruction Efficiency: Neutral vertex technique • Considering only kaon decays with a p0 K X p0 X gg We lookfor the vertex asking • clusters on time: (t - r/c)g1 = (t – r/c)g2 • p0 invariant mass • agreement between kaon flight time and clusters time

  14. charged daughter kaon Efficiency measurements • eG = eTrK eTr sec eV 1 2 Nvn = Neutral vertex sample Nvn&vc = neutral and charged vertex sample Nvn&TrK = tracked K given neutral vertex sample NTrK&nv = neutral vertex given tracked K sample

  15. Reconstruction efficiency • Global vertex efficiency • Tracking efficiency • Vertex efficiency given the kaon track • Methods comparison

  16. Global efficency comparison Normalization sample: tagged events with a neutral vertex in the signal emisphere with the cut: • (40 < r <150) cm , | z | < 150 cm MC reco MC kine

  17. Global efficency comparison MonteCarlo kine vs MonteCarlo reco  large fit window aG = (94.3  0.5) x10-2 bG = (-.13  .22) x10-3 aG = (94.4  0.7) x10-2 bG = (-.07  .31) x10-3 between 12 and 45 nsbetween 15 and 35 ns

  18. Global efficency comparison: normalization sample K X p0for kine efficiency If we normalize the kine efficiency using only theevents K X p0 also absolute normalization is in perfect agreement. The difference is given by the Kmn characterized by high momentum secondary tracks aG = (99.8  0.8) x10-2 bG = (.37 .34) x10-3

  19. Data and MonteCarlo compared The difference between Data and datalike MonteCarlo is due to an imperfect simulation of the correlated background: kaons produce in the first 12 layers of the D.C. adjacent spurious hits along their path, spoiling the tracking performance reco MC Data

  20. Method 2: tracking efficency MonteCarlo kine vs MonteCarlo reco  large fit window More than 1 meter of track MC reco MC kine MC reco MC kine MC reco MC kine

  21. Method 2: tracking efficency MonteCarlo kine vs MonteCarlo reco  large fit window aTRK = (100.0  0.2) x10-2 bTRK = (-.12  0.92) x10-4 aTRK = (100.2 0.3) x10-2 bTRK = (-.15  0.14) x10-3 between 12 and 45 nsbetween 15 and 35 ns

  22. Data and MonteCarlo compared The difference between Data and datalike MonteCarlo is due to an imperfect simulation of the correlated background: kaons produce in the first 12 layers of the D.C. adjacent spurious hits along their path, spoiling the tracking performance reco MC Data

  23. Method 2: vertexing efficency MonteCarlo kine vs MonteCarlo reco  large fit window MC reco MC kine MC reco MC kine

  24. Method 2: vertexing efficency MonteCarlo kine vs MonteCarlo reco  larger fit window aVTX = (94.2  0.4) x10-2 bVTX = (-.35  .19) x10-3 aVTX = (93.7  0.6) x10-2 bVTX = (-.48  .29) x10-3 between 12 and 45 nsbetween 15 and 35 ns

  25. Data and MonteCarlo compared reco MC Data reco MC Data Good agreement

  26. aConf = (100.2  0.6) x10-2 bConf = (.1  .3) x10-3 aconf = (100.  1.) x10-2 bconf = (.19 .43) x10-3 Two methods compared:MC global product

  27. aconf = (100.9  0.7) x10-2 bconf = (.37 .32) x10-3 aconf = (101.  1.) x10-2 bconf = (.40 .46) x10-3 Two methods compared: Data global product

  28. Resolution functions vs lifetime fit • Resolution function evaluated from the MonteCarlo simulation as a function of the charged kaon proper time • Point of Closest Approach (PCA) techinque • Validation of thePCA techinque • After PCA method validation the resolution function evaluated with closest approach technique on MonteCarlo and Data

  29. Resolution functions: MC Fit to the charged kaon proper time using a convolution of an exponential function and a resolution function. But also the resolution function is a function of the proper time Resolution evaluated with the MonteCarlo simulation: Treco – Tkine. Needed a method that can be applied to Data between 8 and 10 ns between 20 and 21 ns

  30. Point of Closest Approach method We search the point of closest approach between the last hit of the kaon track and the first hit of the charged decay particle track reso X reso Y reso Z ALL centered -> no bias on lifetime slope reso Px reso Py reso Pz

  31. reco MC Data PCA proper time resolution: comparison Data-MC Treco – TPCA Data reco MC

  32. Fit procedure We make the fit in the region between 15 and 35 ns. To fit the proper time distribution we construct an histogram ,expected histo, between 12 and 45 ns, in a region larger than the actual fit region to take into account border effects. The number of entries in each bin is given by theintegral of the exponential decay function, which depends on one parameter only,the lifetime, convoluted with theefficiency curve. Then a smearing matrix accounts for the effects of the resolution. We also take into account the tinycorrection to be applied to the efficiencygiven by the ratio of the MonteCarlo datalike and MonteCarlo kine efficiencies. nbins Nexpj = S Csmearij× ei × eicorr × Nitheo i = 1

  33. MC t+ measurement: ° MC reco • Fit Best value between 15 and 28 ns t +MC = (12.403 ± 0.079) ns c2 =1.13 Pc2 =32.6%

  34. MC t+ measurement: ° MC reco • Fit Fit region between 15 and 28 ns The fit reproduce the dist. also well ouside the fit region ! t +MC = (12.403 ± 0.079) ns

  35. MCt+residual evaluation Cons+MC = -0.03 ± 0.16 Pc2 = 49.8%

  36. MC t- measurement ° MC reco • Fit Fit region between 17 and 31 ns t-MC = (12.399 ± 0.079) ns c2 = .93 Pc2 = 52.2

  37. MC t- measurement ° MC reco • Fit Fit region between 17 and 31 ns The fit reproduce the dist. also well ouside the fit region ! t-MC = (12.399 ± 0.079) ns

  38. MCt-residual evaluation Cons+MC = 0.02 ± 0.09 Pc2 = 59.8%

  39. Data t+ measurement ° Data • Fit Fit between 16 and 30 ns t+Data = (12.337 ± 0.066) ns c2 = 1.18 Pc2 = 28.4%

  40. Data t+ measurement ° Data • Fit Fit between 16 and 30 ns The fit reproduce the dist. also well ouside the fit region ! t+Data = (12.337 ± 0.066) ns

  41. Datat+residual evaluation Cons+Data = -0.005 ± 0.035 Pc2 = 56.2%

  42. Data t- measurement ° Data • Fit Between 17 and 31 ns t-Data = (12.388 ± 0.058) ns c2 = 1.1 Pc2 = 35.2%

  43. Data t- measurement ° Data • Fit The fit reproduce the dist. also well ouside the fit region ! Fit between 17 and 31 ns t-Data = (12.388 ± 0.058) ns

  44. Data t- measure Cons-Data = 0.00 ± 0.18 Pc2 = 49.1%

  45. Preliminary systematics check • Fit stability as a function of the range used - done • Fit stability as a function of the bin size - done • Fit stability with or without of the efficiency correction- done • Correction due to a not correct evaluation of the Beem Pipe and Drift Chamber walls thickness- done • Systematic on efficiency - missing

  46. Very preliminary systematics check Systematic uncertainties of the order of 65 ps

  47. 0.024 KLOE Weighted mean between t+andt- t = (12.367±0.044stat ±0.065syst) ns preliminary

  48. Conclusions We obtained tK± = (12.367±0.044stat ±0.065syst)ns which is in agreement with the result obtained with the PDG 2004 fit. We have to complete the systematics check Work is in progress for the “time” measurement

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