Kloe General Meeting University La Sapienza Rome 13

Kloe General Meeting University “La Sapienza” Rome 13 -14/11/2003 Preliminary results on Ks 3 p 0 search M. Martini and S. Miscetti

Preliminary results on Ks 3 p 0 search. . . M. Martini and S. Miscetti Analysis outlook: Ø Comparison Data-MC, starting from DST dk 0, mk 0 ü Data 450 pb-1 ü MC 150 pb-1 (New. Kaon) Ø Kinematic fit procedure Ø Definition of pseudo- 2 to improve S/B ü 2 2 p ü 2 3 p Ø Study of the background shape Data vs MC Ø Adjusted simulation to reproduce observed bkg rate Ø Track veto cut Ø E gamma cut and. . . first conclusions Kloe General Meeting 14/11/2003 M. Martini

Events filter Ø Tuning of acceptance cuts (6 pb-1 Data, MC) looking for highest e while retaining 0 candidates in events with 6 neutral clusters: TW = 3. 5 s ; Ecut = 7 Me. V ; = 22, 5° 58% Ø Standard Kcrash Tag (100 Me. V, large b* window ) Ø Kinematics fit (from S. Giovannella) applied using Ks momentum estimated by Kcrash (and ) by requiring 4 momentum conservation on the Ks side ( 2 fit). Ø Major expected background: KS 0 0 + 2 accidental (or splitted) clusters. Ø In order to improve S/B we defined two pseudo-c 2 to look for KS 2 0 vs KS 3 0 Kloe General Meeting 14/11/2003 M. Martini

Application of Kinematic fit Constraining the Ks side with expected Ks momentum by Kcrash gives good rejection but leaves a sizeable quantity of bkg events!! Kloe General Meeting 14/11/2003 M. Martini

Construction of the 2 2 p Ø 2 2 p is built selecting 4 out of 6 clusters which better satisfy the kinematics of KS into 2 pions decay Ø The kinematics parameters used are: ü Mass distributions ü Opening angle between pions in Kaon C. M. Frame ü Four-momentum conservation KL KS All this is done using the reconstructed cluster parameters before applying the kinematic fit procedure Kloe General Meeting 14/11/2003 M. Martini

Variables used in constructing 2 2 p KS 2 p with 4 gamma final state Red line MC Black dots DATA Kloe General Meeting 14/11/2003 M. Martini

Definition of 2 2 p is the 2 that we build searching the best combination of 4 out of 6 clusters which represents a KS 2 p 0. The best combination is the one minimizing: Kloe General Meeting 14/11/2003 M. Martini

2 3 p At the moment, the 2 3 p is based only on the 3 reconstructed pion masses Data MC Comparison Data-Mc before splash filter Splash filter consists of: - Ngam = 6 Emean < 40 Me. V Mmean < 40 Me. V - Ngam = 4 Emean < 50 Me. V Mmean < 50 Me. V Kloe General Meeting 14/11/2003 M. Martini

2 3 p Data MC Comparison Data-Mc after splash filter Kloe General Meeting 14/11/2003 M. Martini

Data MC comparison of 2 2 p MC 2001 vs 2 3 p 2001: a surprise! Data 2001 In the data, a new category of BKG events (not simulated by the “standard Gatti. Spadaro” Kcrash MC) appears. Their simulation takes into consideration only KL decaying after a cilinder bigger than DCH and smears the KL MC direction with the KL crash resolution observed in data. Kloe General Meeting 14/11/2003 M. Martini

A NEW SIMULATION OF Klcrash (Acci. K) To understand these events whenever no Kcrash is found by the standard Kcrash MC we add the possibility to find a Kcrash applying the standard data cuts (E and b*) Running this new Kcrash simulation on 2002 MC we find other 1320 entries with respect of to the 9657 events already simulated in the 6 prompt clusters sample. There are three different sources of these new BKG events: Ø Acci. Kcrash K crash by accidental Ø T 0 stolen Golden cluster by accidentals Ø Klpipe K crash by KL daughters inside Rt = 25 cm (1) (2) (3) Ngam=6 Ngam=5 Ngam=4 Total events 1320 13677 5314 1 17 17 4 2 130 255 7 3 139 217 28 2*3 77 64 4 1*2 --- 1 0 1*2*3 --- 1 0 1+2+3 207 420 35 Kloe General Meeting 14/11/2003 M. Martini

2 2 p vs 2 3 p 2002 sample Data MC Ks 2 pi MC Ks 3 pi Kloe General Meeting 14/11/2003 M. Martini

Comparison “Data-MC” 2 2 p, no 2 cut All 23 p Normalization with 6 g rate reasonable in the overall plot but missing to reproduce the observed rate in these regions Kloe General Meeting 14/11/2003 23 p < 80 23 p > 80 23 p < 200 M. Martini

Adjusted simulation Data MC Kcra. MC Acci. K Normalization kk 1 23 p<80 Normalization kk 2 23 p<200 Data Kloe General Meeting 14/11/2003 MC Kcra. MC Acci. K M. Martini

Adjusted normalization Normalization: ID 1 = Data ; ID 2 = MC(Kcrash) ; ID 3 = MC(Acci. K) ID 1 = a 1 ID 2 + a 2 ID 3 Where: Ndata = Number of entries of the 2 fit plot for data Nmc = Number of entries of the 2 fit plot for mc Normalization from two different plots: Ø kk 1 Ø kk 2 Ø kkm Coefficients calculated from 2 2 p with 2 3 p less then 80 Coefficients calculated from 2 2 p with 2 3 p less then 200 The average value between kk 1 and kk 2 Kloe General Meeting 14/11/2003 M. Martini

Comparison “Data-MC” 2 2 p, no 2 cut All 23 p > 80 Normalization with kk 1 values 23 p < 80 Kloe General Meeting 14/11/2003 23 p < 200 M. Martini

Comparison “Data-MC” 2 2 p, no 2 cut All 23 p > 80 Normalization with kk 2 values 23 p < 80 Kloe General Meeting 14/11/2003 23 p < 200 M. Martini

Comparison “Data-MC” 2 2 p, no 2 cut All 23 p > 80 Normalization with kkm values 23 p < 80 Kloe General Meeting 14/11/2003 23 p < 200 M. Martini

Comparison “Data-MC” 2 fit, no 2 cut Result of normalization FIT: KK 1 KK 2 Kmed Kcra MC 1. 120 1. 129 1. 123 Acci. Kcra 2. 881 2. 406 2. 643 Kloe General Meeting 14/11/2003 M. Martini

Comparison “Data-MC” 2 3 p, no 2 cut All 22 p > 40 Normalization with 6 g rate 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini

Comparison “Data-MC” 2 3 p, no 2 cut All 22 p > 40 Normalization with kk 1 values 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini

Comparison “Data-MC” 2 3 p, no 2 cut All 22 p > 40 Normalization with kk 2 values 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini

Comparison “Data-MC” 2 3 p, no 2 cut All 22 p > 40 Normalization with kkm values 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini

Definition of the Signal box Up Sbox Down Kloe General Meeting 14/11/2003 Cup CSbox Cdown M. Martini

Comparison Data-MC As explained before we use this sample without any FIT to check the reliability of the “adjusted” simulation on reproducing the rate in the signal and control boxes. NO CUTS on c 2 fit. BOX Name Ndata Nmc kkm Nmc kk 1 Nmc kk 2 Sbox 304± 17 304. 3 326. 9 282. 2 Up 456± 21 517. 2 562. 6 472. 1 Down 356± 19 370. 5 372. 2 372. 7 CSbox 5141± 72 5139. 6 5145. 7 5190. 5 Cup 10523± 103 10452. 9 10469. 3 10552. 1 Cdown 22948± 151 22878. 9 22835. 3 23185. 5 Kloe General Meeting 14/11/2003 M. Martini

Comparison “Data-MC” 2 fit, 2<30 Kloe General Meeting 14/11/2003 M. Martini

Comparison “Data-MC” 2 3 p, 2<30 All 22 p > 40 Normalization with kk 1 values 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini

Comparison “Data-MC” 2 3 p, 2<30 All 22 p > 40 Normalization with kk 2 values 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini

Comparison “Data-MC” 2 3 p, 2<30 All 22 p > 40 Normalization with kkm values 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini

Comparison Data-MC Check reliability of the “adjusted” simulation when a 2<30 cut is applied. BOX Name Ndata Nmc kkm Nmc kk 1 Nmc kk 2 Sbox 18± 4 21. 9 23. 5 20. 3 Up 1± 1 0. 0 Down 54± 7 55. 8 56. 2 56. 1 CSbox 820± 29 856. 2 867. 5 Cup 32± 6 20. 2 20. 5 Cdown 13278± 115 12746. 1 12716. 2 12922. 9 Kloe General Meeting 14/11/2003 M. Martini

Next cuts. . . TRKOK All 22 p Counting only tracks with: r(PCA)<4 cm Z(PCA)<10 cm To reject tracks from qcal. Veto events with TRKOK>0 Kloe General Meeting 14/11/2003 14< 22 p < 40 22 p > 40 22 p < 14 M. Martini

Kinematic distributions after 2 2 p, 2 3 p § Another byproduct of the 2 2 p variable is that we can test if the energy associated to the four selected photons looks like coming from KS 2 p 0 q This is a really good discriminating variable between the two processes as shown here. q Cutting at the level of the blue arrow reduces the background to ½ without touching the signal. q A little harder cut can also be very useful to max. the Upper. Limit ( study will follow by MC) K meeting 14/10/2003 --- Data no 2 cut • Data 2 < 30 --- MCBG no 2 cut MCBG 2 < 30 MCSIG M. Martini

Study of Eg. CUT --- MCBG no 2 cut • MCBG 2 < 30 • MCGG 2 + TRKOK MCSIG At the end of analysis: ü 2 < 30 ü TRKOK ü Signal Box Applying Eg. CUT<9 only Acci. K remain in Signal Region Kloe General Meeting 14/11/2003 Kcra. MC Acci. K M. Martini

Next cuts. . . Eg. CUT All 2 2 p 22 p > 40 TRKOK+Eg. CUT+ 2 Kloe General Meeting 14/11/2003 14< 22 p < 40 22 p < 14 M. Martini

Candidates. . . At the end of analysis we have: Ø 5 candidates from “Data” @ 450 pb-1 Ø 3 expected from “MC” @ 150 pb-1 Nrun: 24051 NTracks: 0 Nev: 7110735 -----------------------Reconstructed pions masses: M 1 = 114. 5806 Me. V M 2 = 125. 6421 Me. V M 3 = 108. 1511 Me. V -----------------------Chi 2 fit: 17. 32738 Chi 2 pair: 17. 72474 Chi 3 pair: 73. 09442 Number of kcrash: 1 Ekcra: 210. 6075 Me. V Beta Kcra: . 2319278 Number of clusters: 9 -----------------------Clusters parameters: Cluster 1 2 3 4 5 6 Energy (Me. V) 122. 7134 112. 8406 87. 42661 39. 42066 118. 7905 35. 05759 Nsigma. 3378006 1. 413125 1. 504362 1. 709174 1. 757564 1. 826631 Angle 1. 265235. 7238826 2. 373147 2. 366150 2. 336169 2. 369749 ------------------------ Kloe General Meeting 14/11/2003 M. Martini

Preliminary calculation of the Upper Limit. . . The efficiencies are: The number of events with 4 g from data are: To calculate the BR we can use: Only suppose the Kcrash is the same in 2 p and 3 p Kloe General Meeting 14/11/2003 M. Martini

Preliminary calculation of the Upper Limit. . . From P. D. G. 2002: Assuming Poissonian statistics and using the upper end of confidence interval we found: Mean expected background = 3 Events observed = 5 Substituting the values: Very Preliminary Kloe General Meeting 14/11/2003 Very Preliminary M. Martini

Conclusions ØAfter MC adjustment reasonable agreement Data-Mc ØNext weeks ü Run all Newkaon statistics (400 pb-1) ü Estimate MC errors ü Search the best signal box ü Definitive Upper limit Kloe General Meeting 14/11/2003 M. Martini

2 2 p vs 2 3 p MC 2001 Kloe General Meeting 14/11/2003 MC 2002 M. Martini

Comparison “Data-MC” 2 fit, 2<100 6 g Norm. kkm Norm. kk 1 Norm. kk 2 Norm. Kloe General Meeting 14/11/2003 M. Martini

Comparison Data-MC Check reliability of the “adjusted” simulation when a 2<100 cut is applied. BOX Name Ndata Nmc kkm Nmc kk 1 Nmc kk 2 Sbox 47± 7 64. 7 57. 9 71. 6 Up 5± 2 5. 1 3. 9 6. 4 Down 139± 12 145. 7 144. 9 146. 5 CSbox 1307± 36 1377. 0 1388. 0 1365. 0 Cup 159± 13 125. 4 126. 9 124. 0 Cdown 9418± 97 9592. 0 9681. 0 9502. 0 Kloe General Meeting 14/11/2003 M. Martini

Comparison “Data-MC” 2 3 p, 2<100 All 22 p > 40 Normalization with 6 g rate 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini

Comparison “Data-MC” 2 3 p, 2<100 All 22 p > 40 Normalization with kk 1 values 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini

Comparison “Data-MC” 2 3 p, 2<100 All 22 p > 40 Normalization with kk 2 values 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini

Comparison “Data-MC” 2 3 p, 2<100 All 22 p > 40 Normalization with kkm values 14< 22 p < 40 Kloe General Meeting 14/11/2003 22 p < 14 M. Martini