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The K L  p 0 p 0 decays in KLOE

The K L  p 0 p 0 decays in KLOE. Introduction gg pairing Discriminant variables K L  p 0 p 0 counting : 3,4,5 g ’s samples Control samples for efficiencies Stability in the F.V. Stability in time K L  p 0 p 0 counting: summary table Efficiency evaluation Dalitz pairs

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The K L  p 0 p 0 decays in KLOE

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  1. The KLp0p0 decays in KLOE Introduction gg pairing Discriminant variables KLp0p0 counting : 3,4,5 g’s samples Control samples for efficiencies Stability in the F.V. Stability in time KLp0p0 counting: summary table Efficiency evaluation Dalitz pairs Ks from regeneration KLp0p0p0 events Ratio of the B.R.

  2. Introduction KLp0p0 selection against KLp0p0p0decays is based on the recognition of the 2-body kinematics Modest energy resolution for p0 is further spoiled by the combinatorial of the gg pairing. The gg pairing efficiency has been improved from 60% to 98% taking into account not only the residuals to the p0mass, but also the residuals to the Kaon energy and momenta, normalized to the covariance matrix The results have been obtained using as discriminant variable the ratio of the likelihood (RTL), to have 1or2p0s from 2-body decay against the hypothesis to have 1or2p0s without any constraint on the energy. The counting is done comparing the signal and background in a large rtl-window.

  3. Miscellanea Generalized c2 function for gg pairing KLp0p0andKLp0p0p0 DTT V-1 DT Mp0  T I = E Ia E Ib (1-cosdI) I=1,2,(3) T E = SI EIa (E Ib ) T Px= SI PxIa (E Ib cosaI, ) T Py= SI PxIa (E Ib cosbI, ) KLp0p0p0only Covariance matrix calculated by the derivatives on respect (EI, xI, yI, zI, RK) s(EI ), s(xI ), s(yI ), s(zI ) have been parameterized as in the Simona work on the p0p0g analysis s(RK ) from the KLp+p-p0 analysis For each pairing there are 2(n(p0)) values of the function : the minimum is kept PK 

  4. p0 mass distribution normalized to the s 3p0 - Data 2p0 - 3p0 mc events

  5. Miscellanea Test Hypothesis : H0 2-body kinematics - KLp0p0event Alternative hypothesis : H1 p0X Egi = gk/4/ai (1 (1 – D) ) ai = gk (1 -bk cos(ki) ) D = 8 ai aj m2(p0)/ m2(K0) / (1 – cos(ji) ) Egi = m2(p0) / Egj / 2. / (1 – cos(ji) ) L(np0 ) =I exp(-(Emesgi –Egi)2 / sgi2) / (sgi (2p) ) I=1, …2n sgi parameterized always in the same way. LRatio = max(L| H0 )/ max(L| H1 ) -2 log(LRatio) used (RTL) For each pairing there are 2(n(p0)) values of the function : the maximum is kept

  6. KLp0p0 counting Tag criteria, fiducial volume : same as in the KLgg analysis Track veto criteria : reject the events having tracks not associated with KSp+p- chain Efficiency measured with KLp0p0p0  6g 0.8131 0.0002 Large RTL window to work at high efficiency (0.9 for 4g sample) Monte Carlo efficiency controlled by the signal selected with the global fit Signal counting by the fit of signal+bck distribution of RTL Monte Carlo distribution scale adjusted using QCAL events for the background AND events selected by the global fit for the signal Counting controlled by the fit of signal+bck distribution of DE for events selected by global fit

  7. RTL : why? Black histo : Data (4clusters) Red Histo : KLp0p0 Green Histo: Shape without KLp0p0 contribution Black histo : Data (4clusters) Red Histo : KLp0p0 Green Histo: Shape without KLp0p0 contribution RTL Maximum of s/(b+s) reached @ max(s) Smoother behaviour of the background in the signal region DEcalc

  8. Data-MonteCarlo comparison after scaling Signal-Rich Sample Max_dist 1.2% prob 58% Qcal tagged background Max_dist 1.6% prob 13%

  9. 5cluster sample DEecal>0 required c2=1.2 0.151±0.020 1023±120 events Mc e =0.5x0.86

  10. 3cluster sample c2=1.4 0.067±0.005 4276±320 events Mc e =0.77 DE(p0)>0. required c2=1.0 0.26±0.02 2350±180 events Mc e =0.5x0.77

  11. 4cluster sample c2=0.9 0.157±0.003 18823±360 events e =0.94(data)x0.95(mc)

  12. Events selected by global fit RTL c2=1.1 0.834±0.015 13197±240 events DEcalc c2=0.7 0.792±0.020 12536±300 events

  13. Fiducial Volume p0p0 vs p0p0 p0events p0p0 + bck (RTL < 10. required) distribution on the entire volume selected for the analysis Background level greater near the calorimeter distribution for 30<Distance<140. blue points : p0p0 + bck (RTL < 10. required) Black histo : p0p0 p0(sample with > 4 clusters) Entries are those of the 3p0 sample, the blue points have been normalized to an equal number of events Distance from the origin of the neutral vertex

  14. Fiducial Volume Control : Signal counting (4g) / 3p0 Signal (4-cluster sample)/ 3p0 in 9 (R, f) not-overlapping regions of the fiducial volume 1 . R<68.cm, f <- p/3. 2 . R<68.cm, - p /3.< f < p /3. 3 . R<68.cm, f > p /3. 4 . 68< R<111.cm, f <- p /3. 5 . 68<R<111.cm, - p /3.< f < p/3. 6 . 68<R<111.cm, f > p/3. 7 . R>111.cm, f <- p /3. 8 . R>111.cm, - p/3 < f < p /3. 9. R>111.cm, f > p /3.

  15. Time Stability Control : Signal counting (4g) / 3p0 Signal (4-cluster sample)/ 3p0 for 9 data subsamples 1 . Nrun< 23800 2 . 23800<Nrun< 24500 3 . 24500<Nrun< 25050 4 . 25050<Nrun< 25450 5 . 25450<Nrun< 25900 6 . 25900<Nrun< 26200 7 . 26200<Nrun< 26550 8 . 26550<Nrun< 26800 9 . Nrun>26800

  16. KL KSp0p0 counting N reg_gas / N kl p0p0 = 0.24  0.04 Event topology measured @ radial position of 25 cm (regeneration at the entrance of the DC) 3clust/4clust = 0.85  0.02 (see next slide) The high percentage of 3cluster events due to the “bad” position of the KSp0p0 vertices on the KL flight direction I assume for the regenerated events an extra-source of loss of clusters connected to the neutral vertex P3reg = P3 (1.- ploss)3 + P4 (1.- ploss)3 ploss+ P5(1.- ploss)3 p2loss P4reg = P4 (1.- ploss)4 + P5(1.- ploss)4 ploss P3reg /P4reg = 0.85 ; P3,P4 ,P5 are the percentages for the “normal” p0p0 events. From above, I obtain: Ploss = 0.129 P3reg = 0.38 P4reg= 0.45 P5reg = 0.05 I use the 3cluster and 4cluster events on the regeneration-peak, e.g. the events in a window 0.4 cm wide aroun 25 cm, to measure the percentages of events counted as signal in the analysis: 4-cluster events: 2500 regenerated events in the window : fit output: 525  30 (c2=1.1) 3-cluster events: 2066 regenerated events in the window : fit output: 626  50 (c2=1.1) The contribution to the 5cluster sample is negligible e3reg = 0.30  0.02 e4reg = 0.21  0.01 3clusters 0.24 x 0.38 x 0.30 N(KLp0p0 ) = 0.03 N(KLp0p0 ) 4clusters 0.24 x 0.45 x 0.21 N(KLp0p0 ) = 0.02 N(KLp0p0 ) @30%

  17. 3cluster events Fit: 18773 530 events Any cluster multiplicity > 2 Fit: 47360  900 events Fit: 22090  620 events

  18. KLp0p0 counting Fit Regen Track veto Final MC from data Dalitz Final 3cluster sample 4276  320 -684  200 4418  460 4418  460 1326 -220 5524 5cluster sample 1023  130 1258  160 1258  160 1667 2925 4cluster sample 18823  350 -684  200 22308  500 23732  530 1151 24883 TOT 24122  490 27984  700 29408  720 4144 -220 33332 3924 MC - >5cluster 140 Dalitz decays (810  30) 30218  720 4064 34282 KLp0p0p0 7650900  10000 N(KLp0p0)/N(KLp0p0p0 ) = ( 4.48  0.11  ) 10 -3

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