Outline: (1) The data sample (2) Some news on the analysis method (3) Efficiency revised (4) Background revised (5) Data: spectrum + “phi-curve” (6) Data-MC comparison      with         2000 data  197 candidates / 16 pb -1  4  4 estimated background  19% efficiency C.Bini D.Leone KLOE Memo /02 KLOE Collab. Phys.Lett.B536 (2002) spectrum + combined fit

(1)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 “good” runs “peak” runs : pb -1 full sample “good” runs “peak” runs Lum (nb -1 / 0.2 MeV) vs  s 100 evts 1 evt Full data sample  pb -1 “good”  pb -1 “peak”

(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 X k meas = measured quantities (momenta, energies, positions, times) X k fit = parameters of the fit N C = number of constraints = (3) C i = constraints (functions of the parameters) i = arbitrary parameters (in principle   ) The result has not to depend on MC data 1/  (MeV) On data and Montecarlo samples Studied the dependence: Large “plateau” observed for data and Montecarlo:  Small   more events enter (mostly background)  Large   loss of events (MINUIT “crisis”)  values at “plateau center”

(3) Efficiency revised Used MC with accele default (based on 2000) Corrections on data / MC for photons and tracks (based on 2000) Weighted M(  ) 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(  2 ) > 5% at least 1 “good” combination kin.fit 2 (on all “good” combinations) p(  2 ) > 5% E(rad) > 20 MeV M(  ) (MeV)

(4) Background revised Expected background ~ few % from MC but checked with data Main sources: final state  (equiv.) MC available L eq             9.6 pb580 e + e -             4.7 nb  70             8.4 nb30     K S K L            50 nb4.2    K S K L                        e 20 nb9.3 The K S K L final state are considered for K L decaying R < 25 cm Results of selection chain application: 2   events  11 events on the “peak” sample 1 K S K L            event  41 events on the “peak” sample No events from other channels  < 100 events (notice: 1   enters for an accidental; 1   for a splitting; the K S K L for a low energy photon lost)

Distribution of M(       ) after kin.fit-1 (10 entries per event): MC expectations for signal and background Same distribution from 2002 data sample after kin.fit-1: events (only ~3000 of them are “good” signal events)

Try to describe the data distribution with Sum of: MC (signal +  background + Ksn background). (solid) data (dashed) MC sum It works but:  =  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%

(5) The data: Number of eventsEvents / L “good” sample “peak” sample Assuming the same efficiency  0.28 vs  0.21 Difference = 0.83  0.35  scan results:

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

Is it a spectrum compatible with a resonance ? Take away the signature of the radiative decay, plotting not N(M  ) but M  (MeV) Simple fit with Breit-Wigner M R = 985  1 MeV (PDG   1.2 MeV)  R = 33  1 MeV (PDG  50  100 MeV)

Dalitz plot density distribution: M(  ) vs. M(  ) Expected signals from  and     a 0 region Distribution of M(  ) (5 MeV bins) : signal of  ?

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

(d) cos  rad distribution: comparison with (1+ cos  rad 2 ): try fit with: A(1+x 2 )+B(1-x 2 ) If dist ~ (1+ cos  rad 2 ) B=0 data need B  0  deviation from (1+ cos  rad 2 ) Solid = MC Points = data 2002 Curve = A(1+x 2 )

Conclusions: (0) Some improvement to the data sample (1) Work on new Montecarlo with: improved statistics realistic background  Understand 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