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

INFN & UNIVERSITY OF PERUGIA Outline Theoretical Motivations The BaBar Detector Data Sample Cut-Based Event Selection Likelihood Approach and Neural Network Method Results and Statistical Interpretation Conclusions LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

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 l1 ni l2 LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

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 l1 l2 l1 ni l2 _Yukawa coupling of the order of top_Yukawa coupling LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

INFN & UNIVERSITY OF PERUGIA BaBar Detector 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 1.5 T Magnet EM Calorimeter 6580 CsI crystals e+ ID, p0 and g reco LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

INFN & UNIVERSITY OF PERUGIA 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 LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

Cut-Based Events Selection Charged track: none of the BaBar e-Id selectors allowed Neutral cluster: Photon-quality cuts and p0 veto for 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 ): EEMC cluster > 1 GeV #IFR hit layers > 5 m g g m LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

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 Efficiency = (8.46 ± 0.85)% Background events: expected 11.10 ± 1.15 observed 12 (111-1,13-1) 1-1 topology 3-1 topology CM m momentunm 0.9 - 4.5 GeV/c 1.1-4.5 GeV/c Minimum CM g energy 0.9 GeV minimum pT CMmiss 1.2 GeV/c 0.6 GeV/c cosqCMmiss -0.85 - 0.75 -0.90 – 0.80 maximum Mrecoil 1.65 GeV/c2 1.75 GeV/c2 DE -0.06 – 0.10 GeV -0.09 – 0.08 GeV Mmgfit window ±2.0 s (Mmgfit) LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

INFN & UNIVERSITY OF PERUGIA 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)  LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

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 LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

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, of signal 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 # of signal events Sensitivity Upper Limit 13x10-8 6.8x10-8 Sensitivity Upper Limit CLS+B 11.9x10-8 9.4x10-8 CLS 16.1x10-8 14.6x10-8 LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA

INFN & UNIVERSITY OF PERUGIA Conclusions 229.4 fb-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 LNF SUMMER SCHOOL 2005 MAY 19 2005 ELISA MANONI INFN & UNIVERSITY OF PERUGIA