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Search for LFV at BaBar

Search for LFV at BaBar. Marcello A. Giorgi (on behalf of Babar collaboration) Università di Pisa & INFN Pisa September 25,2008. LFV in tau decay. Standard Model allows LFV.

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Search for LFV at BaBar

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  1. Search for LFV at BaBar Marcello A. Giorgi (on behalf of Babar collaboration)Università di Pisa & INFN Pisa September 25,2008 Marcello A. Giorgi

  2. LFV in tau decay Standard Model allows LFV. In charged leptons it can occur in loops with expected low branching fractions. Es: expected Br (tmg) <O (10-40) Even less in 3 leptons For this contribution a b But with all contributions becomes larger than expect c b c c a Observable lepton decays with FV will allow a clear indication of New Physics. Many New Physis models predict strong enhancement of violating decays of muons and taus. In many models measurable and even quite large t BR [O(10-8)] are expected. Marcello A. Giorgi

  3. Some model predictions In SUSY LFV decays are generated via slepton mixing Marcello A. Giorgi

  4. Tau factory At e+e- asymmetric B factories KEKB and PEPII the high luminosity allows huge t pair data samples usable for LFV search in many channels. Marcello A. Giorgi

  5. Babar-PEPII Integrated Luminosity t- e+ e- t+ PEPII and KEKB are Asymmetric t Factories at 10.58GeV center of mass Energy Data sample used in the analysis is 376 fb-1, 346M t pairs Recorded Luminosity 531fb-1488M t pairs on disk Marcello A. Giorgi

  6. 3-1 PRONG selection t- 1 prong Q=-1 selection t- e+ e- e+ e- t+ t+ 3 prong Q=+1 selection Hemisphere#2 Hemisphere#1 Thrust axis Babar lab. reference system Center of mass system Marcello A. Giorgi

  7. Preselection Efficiency (%) Marcello A. Giorgi

  8. Analysis Strategy (Of course blind analysis) E2 E1 M1 M2 Definition of LB, SB in (DM,DE) plane • The signal and background events are scattered in the plane where “Signal Boxes” (SB) are optimized for the lowest expected upper limit (UL). The area around SB, that includes sidebands needed for Bkg normalization on data, defines the large box (LB). • Different signal regions SB are used for different signal channel. • Resolution and radiative effects smear the DM-DE distributions of each signal channel in different way, and limits of SB in (DM,DE) plane are therefore separately optimized. • The expected Bkg is estimated from sidebands • Borders of LB are the same for all channels DMvsDM Marcello A. Giorgi

  9. Adding Particle Identification (PID) • Bhabha and di-muon are modeled from control samples: PID efficiency same as for data • Average PID efficiency for Muons 65% • Average PID efficiency for electrons 81 % PID efficiency Low PID efficiency due to presence of softm(in m-m+m-channel 35 % muons are slow ) Marcello A. Giorgi

  10. Event Selection • Mass of 1-prong side (with missing momentum) 0.3 <m1pr (GeV)<3.0 for (e-e+e-) (m-e+e-) 0.5 <m1pr (GeV)<2.5 • Momentum of 1-prong track p1cms <4.8 GeV • No tracks in the 3 prong side identified as Kaons • Total transverse momentum in the c.m.s. : • pTcms>0.4GeV for (e-e+e-) and (e-m+m-) • pTcms>0.2GeV for (m-e+e-) • To reject BhaBha in (e-e+e-) and (e-m+m-), one prong track should not be identified as electron. • To reject di-muons in (m-e+e-) and (m-m+m-), one prong track should not be identified as muon. Efficiency (%) Marcello A. Giorgi

  11. Backgrounds qq (uds, cc) (bb negligible) uniform DM, DE < 0 (MC) Bhabha di-muon Uniform DM DE  0 (MC) tt backgrounds DM < 0 DE < 0 (MC) t 3l Data candidates Marcello A. Giorgi

  12. Bidimensional Bkg Estimation Hadronic (uds + cc) backgrounds estimated using a two-dimensional (DM, DE) PDF as product of two one-dimensional (PMh, PEh) PDF. To avoid correlations a choice of rotated variables is made (empirical parametrization): PM’ is a bifurcated Gaussian. DE0’[and s(DE’)] , a are free parameters. Tau backgrounds are estimated with a similar likelihood fit but with same parameterization , but now 2 parameters :a andb. (e-e+e-) and (e-m+m-) have large QED background contributions the estimation is done by using a Reverse PID approach. Control sample is built with events in the great sideband (LB) passing selections,but PID in 1 prong side. PQED is defined as product of two one dimensional PDF on rotated variables PM’ and PE’ . PM’ is a third order polynomial while PE’ is a crystal ball function. A single rotation angle ais used . Marcello A. Giorgi

  13. Expected Events MC fit of PDF shapes of each Bkg component, then normalized on GS (data) extrapolated contribution to SB The number of expected events in the signal region SB are estimated by fitting Background PDF on data in the great sidebands [GS]=[LB-SB] and then integrating the resulting PDF in SB. In sidebands the number of expected events is equal to the number of observed data events. Marcello A. Giorgi

  14. Systematic Uncertainties • Uncertainties on Signal Efficiency • Limited MC statistics introduce a 0.5-0.8% error in estimating efficiency (including phase space production model, ISR and FSR modeling) • Uncertainties in branching fractions used in simulation of generic t background introduce 0.9% error, luminosity and t x-section contribute to 1% uncertainty. • Tracking efficiency and resolution contribute in average 0.25% uncertainty /track, 1. % in total. • Uncertainty on PID efficiency gives a contribution between 1.7% and 10.7% , uncertainty is larger for muons than for electrons. • Uncertainties on Background Estimation • The estimated Background incorporates uncertainties from limited statistics on data sidebands used fin the fit of Bkg PDF, from varying Bkg parametrisation and from MC statistics used in the fit of PDF of each bkg component. • Other systematic uncertainties coming from two-photon contributions are found to be negligible Marcello A. Giorgi

  15. Calculating Upper Limit (UL) SB UL is calculated using Cousins Highland Method [R.D. Cousins and V.L. Highland, Nucl.Instr.Meth.A 320,331 (1992)], and using the Barlow calculator [R.Barlow,Comput.Phys.Commun. 149,97(2002)] DE Toy MC are produced varying Branching Fraction. The events fall part (“signal” ) inside the box and part (“background”) outside the box. BF is varied in the toy MC until the percentage of toy MonteCarlo observing less events than the number observed in the data (Nobs) is 10%. This defines the 90% CL UL DM LB The expected UL is defined as the mean upper limit in assuming we observe a number of events distributed as expected Background. Marcello A. Giorgi

  16. Search for t- l-l+l- NO SIGNALS OBSERVED UL(10-8) • Efficiency ~ 5.5 -12.4 % depend on modes • Background events : 0.3 – 1.3 depend on modes • Total Background = 4.2 ± 0.8 events • Observed events = 6 events • BR(t- l-l+l-) < (3.7-8.0) 10-8 @90% C.L. Phys.Rev.Lett.99:251803,2007 Marcello A. Giorgi

  17. LFV prediction by model ? ? Some predictions are already excluded by the present results. Some can be tested soon by combined statistics of Babar and Belle Marcello A. Giorgi

  18. END Marcello A. Giorgi

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