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Data Analysis Strategy for Extracting Branching Ratios in Particle Physics

This study presents a methodology for measuring branching ratios of particle decays using momentum distribution analysis in the kaon reference frame. It discusses selection efficiencies, reconstruction processes, and different contribution assessments. Various strategies are evaluated with an emphasis on peak fits and efficiency evaluations. Work is ongoing to refine the accuracy and reliability of the results.

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Data Analysis Strategy for Extracting Branching Ratios in Particle Physics

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  1. Status of K±p±p0 E. De Lucia

  2. Strategy • Self-tag on one side using K- (nuclear interactions) • Vertex with 2 tracks in DC on the signal side BR(K±p±p0) = (21,13 ± 0.14)% DBR/BR = 6,6x10-3 Method: Fitting the distribution of the momentum of the secondary track (p*) in the kaon reference frame we can extract BR(K±p±p0) • The selection efficiency is only related to DC reconstruction: • tracking efficiency • vertex efficiency

  3. mn peak vs p*(p mass) pp0 peak fit with p*(m mass) MC MC p* (MeV/c) p* (MeV/c) 2) Use the two “m-clusters” sample • hp:pp0 and mn have the same resolution function • p*(p mass) distribution for mn peak • p*(m mass) distribution for pp0 peak

  4. In order to extract the number of pp0decays? • Check the estimate of the mn background • - require a p0 in the calo and measure the efficiency • Take into account the 3-body decays contribution • - fit the distribution down to 190 MeV in order to have • 3-body decays contribution  only from kl3 channel The fit uses the MC shape for the 3-body decays contribution

  5. MC study of the fit 190 pb-1 pp0 mn

  6. p* (Kpp0 self-tag) p* (Kmn self-tag) p*(pp0) MC-FIT comparison Events/0.5 MeV

  7. p*(pp0) MC different contributions

  8. p* (Kpp0 self-tag) p* (Kmn self-tag) p*(mn) MC-FIT comparison

  9. p*(mn) MC different contributions

  10. The 2m-cluster sample: • reproduces the p*(mn) behavior • but • does not reproduce the p*(pp0) behavior at low and high p* • the fit overestimates 3-body decays and mn contribution • We have two possible strategies to measure BR(pp0): • 3s cut around the peak • require p0 in EMC and use the obtained p* shape

  11. p* (Kpp0 self-tag) p* (Kmn self-tag) • 3s cut around the pp0 peak Differences between p*(pp0)MC and p*(pp0)FIT fractional difference fit 0.02060+/- 0.0002 fractional difference fit 0.0208 +/- 0.0003 fractional difference fit_sel 0.0049 +/- 0.0001 fractional difference fit_sel 0.0048 +/- 0.0001

  12. Differences between p*(mn)MC and p*(mn)FIT p* (Kpp0 self-tag) p* (Kmn self-tag) The mn contribution under the pp0 peak fractional difference fit -0.00413 +/- 6.E-05 fractional difference fit -0.00388 +/- 5.E-05 fractional difference fit_sel -0.00055+/- 2.E-05 fractional difference fit_sel -0.00059 +/- 2.-05

  13. p* (Kpp0 self-tag) p* (Kmn self-tag) The 3-body contribution under the pp0 peak Differences between p*(3-body)MC and p*(3-body)FIT fractional difference fit -0.0288 +/- 0.0005 fractional difference fit -0.0268+/- 0.0006 fractional difference fit_sel -0.0160+/- 0.0004 fractional difference fit_sel -0.0154 +/- 0.0004

  14. p*(pp0)MC p*(pp0)TAG+p0 p* (Kpp0 self-tag) • require p0 in EMC and use the p* shape • Check that p0 requirement does not introduce distortions • evaluate p0 reconstruction efficiency (ep0) with pp0 tag sample • compare DATA to MC (can we use MC ep0 for Kl3 ?) 10 pb-1 pp0 tag sample

  15. p0 reconstruction efficiency (1) plab_secondary pstar_secondary

  16. p0 reconstruction efficiency (2) rvtx_2d rvtx_3d Work in progress but the idea seems good

  17. Exercise1: extract BR(pp0) and BR(mn) from p*DATA 190 pb-1 DATA pp0 ********* BR results with munu tag********** ntag_munu = 13079285 npipi0 = 781313 ernpipi0 = 971.094 BR(pipi0) = 0.198226 +/- 0.000442406 npipi0_fromsel = 832834 ernpipi0 = 971.094 BR(pipi0) fromsel = 0.211297 +/- 0.000524439 nmunu = 2.49387E+06 ernmunu = 1619.88 BR(munu) = 0.604953 +/- 0.000728481 mn There is a problem in the efficiencies……….

  18. Exercise2: extract BR(pp0) and BR(mn) from p*DATA To use the efficiency evaluated directly from data  require track to cluster association pp0 10 pb-1 mn

  19. ********* BR results with munu tag********** ntag_munu = 410065 npipi0 = 22945.7 ernpipi0 = 163.861 BR(pipi0) = 0.185681 +/- 0.00139935 npipi0_fromsel = 25180.7 ernpipi0 = 163.861 BR(pipi0) fromsel = 0.203767 +/- 0.00163508 nmunu = 81280.8 ernmunu = 289.429 BR(munu) = 0.628879 +/- 0.00252102 emn from2m-cluster ********* BR results with pipi0 tag********** ntag_pipi0 = 312400 npipi0 = 16983.7 ernpipi0 = 146.819 BR(pipi0) = 0.182244 +/- 0.00181808 npipi0_fromsel = 18628.2 ernpipi0 = 146.819 BR(pipi0) fromsel = 0.19989 +/- 0.00203922 nmunu = 61468.3 ernmunu = 252.179 BR(munu) = 0.627028 +/- 0.00380777 epp0 from emn and shift (eMCmn -eMCpp0 )

  20. Exercise0 : extract BR(pp0) and BR(mn) from p*MC 190 pb-1 pp0 ********* BR results with munu tag********** ntag_munu = 6530745 npipi0 = 433366 ernpipi0 = 3314.73 BR(pipi0) = 0.202514 +/- 0.00157059 npipi0_fromsel = 455826 ernpipi0 = 3314.73 BR(pipi0) fromsel = 0.21301 +/- 0.000478008 nmunu = 1443008 ernmunu = 1556.9 BR(munu) = 0.647016 +/- 0.000860994 mn

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