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Outline: (1) The data sample 2001 + 2002 (2) Some news on the analysis method

f  hp 0 g with h  p + p - p 0. 2000 data  197 candidates / 16 pb -1  4  4 estimated background  19% efficiency C.Bini D.Leone KLOE Memo 250 04/02 KLOE Collab. Phys.Lett.B536 (2002). spectrum + combined fit. Outline:

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Outline: (1) The data sample 2001 + 2002 (2) Some news on the analysis method

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  1. f  hp0g with h  p+p-p0 2000 data  197 candidates / 16 pb-1  4  4 estimated background  19% efficiency C.Bini D.Leone KLOE Memo 250 04/02 KLOE Collab. Phys.Lett.B536 (2002) spectrum + combined fit Outline: (1) The data sample 2001 + 2002 (2) Some news on the analysis method (3) Efficiency revised (4) Background revised (5) Data: spectrum + “phi-curve” (6) Data-MC comparison

  2. The data sample • “good” runs: • luminosity value ok • good s value  (a) used in kin.fits •  (b) for “phi-curve” • removed trigger problems (KLOE Memo 281) • “peak” runs • 1018< s <1021 MeV 2001: pb-1 full sample 140.4 “good” runs 137.0 “peak” runs 136.4 100 evts 1 evt 2002: pb-1 full sample 264.9 “good” runs 260.8 “peak” runs 245.2 100 evts 1 evt Lum (nb-1 / 0.2 MeV) vss Full data sample  397.8 pb-1 “good”  381.6 pb-1 “peak”

  3. (2) Some news on the analysis method Kinematical fits are done numerically using MINUIT (“penalty function method”) N = number of measurements per event = 3X2 + 5X5 = 31 Xkmeas = measured quantities (momenta, energies, positions, times) Xkfit = parameters of the fit NC = number of constraints = 4 + 5 + (3) Ci = constraints (functions of the parameters) li = arbitrary parameters (in principle   ) The result has not to depend on l • On data and Montecarlo samples • Studied the l dependence: • Large “plateau” observed for data • and Montecarlo: • Small l  more events enter (mostly background) • Large l  loss of events (MINUIT “crisis”) MC data l values at “plateau center” 1/l (MeV)

  4. (3) Efficiency revised • Used MC with accele default (based on 2000) • Corrections on data / MC for photons and tracks (based on 2000) • Weighted M(hp) distribution using the curve obtained from 2000 data • Cuts: • 2T from vertex ( R < X cm |Z| < Y cm) BPOS used • 5 photons ( > 10 MeV ) • kin.fit 1 p(c2) > 5% • at least 1 “good” combination • kin.fit 2 (on all “good” combinations) p(c2) > 5% • E(rad) > 20 MeV M(hp) (MeV)

  5. (4) Background revised • Expected background ~ few % from MC but checked with data • Main sources: • final state s(equiv.) MC available Leq • f  hp0g  p+p-g p0 9.6 pb 580 • e+e- wp0  p+p-p0 p0 4.7 nb70 • f  hg  p+p-p0 g 8.4 nb 30 • f  KSKL  p+p-p0 p0p0 50 nb 4.2 • f  KSKL  p0p0 p+p-p0  p0p0 pmn  p0p0 pen 20 nb 9.3 • The KSKL final state are considered for KL decaying R < 25 cm Results of selection chain application: 2 wp0 events  11 events on the “peak” sample 1 KSKL  p0p0 p+p-p0 event  41 events on the “peak” sample No events from other channels  < 100 events (notice: 1 wp0 enters for an accidental; 1 wp0 for a splitting; the KSKL for a low energy photon lost)

  6. Distribution of M(p+p-p0 ) after kin.fit-1 (10 entries per event): MC expectations for signal and background Same distribution from 2002 data sample after kin.fit-1: 15358 events (only ~3000 of them are “good” signal events)

  7. Try to describe the data distribution with Sum of: MC (signal + wp background + Ksn background). (solid) data (dashed) MC sum It works but: wp = wp x 4 Ksn = Ksn x 1.5 Why ? Accidentals and splittings not at work in old MC ? Try with new MC Conclusion: Estimated background between 51 and 105 events / 4200 candidates In the worst case < 3%

  8. (5) The data: Number of events Events / L 2001 2002 2001 2002 “good” sample 1424 2856 10.39 10.95 “peak” sample 1422 275910.4211.25 Assuming the same efficiency 2001 - 2002 10.42  0.28 vs. 11.25  0.21 Difference = 0.83  0.35 f scan results:

  9. Comparison 2001-2002 (normalized to luminosity) Spectrum 2001+2002 [4181 evts] compared to 2000 [197 evts] (normalized to luminosity and bin size) Raw spectra: only “peak” samples

  10. Is it a spectrum compatible with a resonance ? Take away the signature of the radiative decay, plotting not N(Mhp) but Mhp (MeV) Simple fit with Breit-Wigner MR = 985  1 MeV (PDG  984.7  1.2 MeV) GR = 33  1 MeV (PDG  50 100 MeV)

  11. Dalitz plot density distribution: M(hg) vs. M(hp) Expected signals from rp and wp r (w)  hg a0 region Distribution of M(hg) (5 MeV bins) : signal of wp ?

  12. (6) Data – MC comparison. (a) tracks and photon distributions (b) c2 probability distributions: Fit-1 and Fit-2

  13. (d) cosqrad distribution: comparison with (1+ cosqrad2): try fit with: A(1+x2)+B(1-x2) If dist ~ (1+ cosqrad2) B=0 data need B0  deviation from (1+ cosqrad2) Solid = MC Points = data 2002 Curve = A(1+x2)

  14. Conclusions: • (0) Some improvement to the data sample • (1) Work on new Montecarlo with: • improved statistics • realistic background •  Understand 2001-2002 discrepancy • 1% estimate of background (2) Track and photon data/MC efficiency • (3) Estimate of BR with more stable efficiency • (4) Fit as 1 year ago • Compare with 5 photons analysis

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