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Search for Lepton Flavour Violation in the decay  →  @ BaBar

Search for Lepton Flavour Violation in the decay  →  @ BaBar. ELISA MANONI INFN & UNIVERSITY OF PERUGIA LNF SUMMER SCHOOL MAY 19 2005. Outline. Theoretical Motivations The BaBar Detector Data Sample Cut-Based Event Selection Likelihood Approach and Neural Network Method

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Search for Lepton Flavour Violation in the decay  →  @ BaBar

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  1. Search for Lepton Flavour Violation in the decay  → @ BaBar ELISA MANONI INFN & UNIVERSITY OF PERUGIA LNF SUMMER SCHOOL MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  2. Outline • Theoretical Motivations • The BaBar Detector • Data Sample • Cut-Based Event Selection • Likelihood Approach and Neural Network Method • Results and Statistical Interpretation • Conclusions ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  3. l1ni l2 Theoretical Motivations (1) • Lepton Flavour Violation observed in athmosferic and solar neutrino oscillations • LFV processes in the charged lepton sector predicted within Standard Model scenario extended to include finite mn lepton-neutrino couplings strongly suppressed because of mni ~ eV ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  4. Theoretical Motivations (2) • LFV processes with higher BR evidence of physics beyond SM • Prediction of in many SUSY models • Within SUSY SU(10) Right Handed  model: (depending on model parameters) • Predictions with l2 l1 l1 nil2 _Yukawa coupling of the order of top_Yukawa coupling ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  5. B-factory: pairs of mesons are produced almost at rest in the CM frame from: Y(4S) → B+B-, B0B0 • Asymmetrical: the CM is boosted forward by bg ~ 0.55 EM Calorimeter 6580 CsI crystals e+ ID, p0 and g reco 1.5 T Magnet BaBar Detector ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  6. Sample • 229.4 fb-1 data collected by BaBar • center of mass energy ≈ 10.5 GeV • s(e+e- t+t-) ≈ 0.89 nb, similar to s(e+e- bb) • ~ 360M t-pairs • Signal events: • e+e-t+t- with t mg • IFR-identified m • EMC-reconstructed g or reconstructed g->e+e- (converted g) • Main background sources: • e+e-t+t-with tanything else • e+e- m+m-g • e+e-  qq • e+e-  e+e- • e+e-B+B- • e+e-B0B0 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  7. Cut-Based Events Selection • Charged track: • none of the BaBar e-Id selectors allowed • Neutral cluster: • Photon-quality cuts and p0 vetofor EMC • reconstructed g • Photon invariant mass and opening angle cuts for • converted g signal side m- g t- e- e+ • 1-1 topology: • 1 charged track not identified as m • 3-1 topology: • 3 charged tracks • Mrecoil < 2 GeV/c2, |cosqCMmiss| <0.95, pTCMmiss>300MeV/c tag side t+ nt • Preliminary cuts on |Minvmg-mt|, DE≡√s/2-Ecmmg , cosqcmmg • Rejection of “fake missing momentum event” (e+e- m+m-(n)g ): • EEMCcluster > 1 GeV • #IFR hit layers > 5 m g g m ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  8. Efficiency = (8.46 ± 0.85)% • Background events: • expected 11.10 ± 1.15 • observed 12 (111-1,13-1) Kinematic Fit and Final Selection • Kinematic Fit in order to remove background and improve mass resolution • two constraints: • mg vertex • beam energy • cut on distribution of c2 variable • Final Selection: cuts optimization to improve the ratio ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  9. Likelihood method • Four variables chosen: • DE • pT missCM • a≡fmg-ftag-p • DpmgCM≡|pmCM|-|pgCM| to set up a likelihood ratio variable (R) defined as : signal background • Number of events determined from an Unbinned Maximum Likelihood fit to the variables: • Mmgfit, R for topology 1-1 • Mmgfit for topology 3-1 • Efficiency = 10.71% • Background events: • expected 28.5 ± 2.3 • observed 27 (261-1,13-1)  ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  10. Neural Network approach • 5 discriminant variables as inputs for NN: • Mmiss • pCMtag • cosqH • pmissT • mn2 • Different selection criteria for different tag modes: • ttag -> enn • ttag -> egnn • ttag -> mgg • ttag -> hn • ttag -> h≥1p0n • ttag -> 3h≥p0n • Unbinned Maximum Likelihood Fit to Mmgfit • Efficiency = (7.42±0.65)% • Background events: • expected 6.2 ± 0.5 • observed 4 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  11. Results and Statistical Interpretation • Likelihood Method • Definition of a test-statistic variable: • Evaluation of Q in binned 2-dimension Mmgfit vs R plane • Measurement of Qobs on the data sample • Generation of toy MC experiments with an arbitrary value, N’signal, ofsignal events • Evaluation of: • CLS+B=PS+B(Q<Qobs) (pure frequentist method) • CLB=PB(Q<Qobs) • CLS=CLS+B/CLB(modified frequentist method) • Iterating by varying N’signal till 1-CLS is 90% • Neural Network Method • Generation of 10k toy MC with Poisson-distributed number of background (fixed) and segnal events (s,floating) • Iterating by varying s till fitted number of signal events greater than that observed ELISA MANONI INFN & UNIVERSITY OF PERUGIA

  12. Conclusions • 229.4fb-1 data analyzed • Cut Based Selection + • Upper Limit @ 90% CL: • Neural Network approach • Likelihood method • Improvement with • respect to previous • measurements • Some SUSY scenarios excluded by this measurement 2002 BaBar 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

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