Search for b t n with semiexclusive reconstruction
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Search for B  t n with SemiExclusive reconstruction. C.Cartaro, G. De Nardo, F. Fabozzi, L. Lista Università & INFN - Sezione di Napoli. Searches for B  t n at BaBar. Two analyses based on 1999-2002 data sample (81.9 fb-1) First : reconstruct one B meson Semi-Exclusively

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Search for B  t n with SemiExclusive reconstruction

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Search for b t n with semiexclusive reconstruction

Search for B tn with SemiExclusive reconstruction

C.Cartaro, G. De Nardo, F. Fabozzi, L. Lista

Università & INFN - Sezione di Napoli


Searches for b t n at babar

Searches for B tn at BaBar

  • Two analyses

    • based on 1999-2002 data sample (81.9 fb-1)

    • First: reconstruct one B meson

      • Semi-Exclusively

      • In a DlnX mode (X = g, p0, nothing)

    • Then: the recoil is analyzed to search for a B tn decay

    • t decays are reconstructed in the following modes:

SemiExclusive Tags

  • (e, ) (e, ) 

  • (, 0, ) 

SemiLeptonic Tags

  •  (e, ) (e, ) 

  • The samples of the two analyses are statistically independent and the results have been combined

F.Fabozzi


B sample counting

B sample counting

  • In Semi-Exclusive analysis we fully reconstruct a B meson in a B  DXhad mode

  • For the BR determination we need the number of B+B- events with a fully reconstructed B

    • determined with a fit to mES

    • for the systematics we fit also with a Gaussian (4.5% less events)

      • Too conservative? We’ll try to fit with a double Gaussian

  • mES distribution fitted as Argus  Crystal ball

  • B sample counting:

    • NBB = (1.670.09)105

sideband

peak

F.Fabozzi


Events preselection

Events preselection

mESdata, 1GTL & 00

  • Preselections for the interesting channels

    • 1 GTL & 0 p0

    • 1 GTL & 1 p0

    • 3 GTL & 0 p0

  • Then fit to the mES distributions

    • Crystal Ball + Argus

  • Fits after the preselection used for

    • data-MC comparison

    • expected background prediction

  • The shape of the Argus function after the preselection is in agreement with the shape after the full selection

mESdata, 3GTL & 00

mESdata, 1GTL & 10

F.Fabozzi


Selections of tau decays

Selections of tau decays

1-prong events

  • 1 track, 0 0

  • No KS

  • Neutral Energy <110MeV

  • Neutral Bumps < 1

  • Pmissing > 1.2 GeV/c

  • Kaon veto

  • Particle ID

  •  only

    • Lepton veto

    • pc.m.s. > 1.2 GeV/c

  • SemiExcl purity mode > 50%

    •  events

    • decay proceeds via two intermediate resonances, an a1 and a 

    • 3tracks and00

    • Pmissing > 1.2 GeV/c

    • Neutral Energy < 100 MeV

    • Neutral Bumps < 1.5

    • 600 MeV < m(0) < 950 MeV

    • 1.1 GeV < m(+) < 1.6 GeV

    • |p1 + p2 +p3 |> 1.6 GeV/c (c.m.s.)

    • Lepton & kaon veto

    • SemiExcl purity mode > 30%

     0 events

    decay proceeds via an intermediate 

    • 1 track and 10

    • pmissing> 1.4 GeV/c

    • Neutral Energy < 100MeV

    • 0.55 GeV < m(0) < 1 GeV

    • SemiExcl purity mode > 50%

    • Optimized for the best upper limit

    • Rejecting events with pmiss in the beam pipe?

    F.Fabozzi


    Integrated purity cut

    Integrated purity cut

    • For each selection we apply a cut on the “nominal”integrated purity of the Breco side

      • Different from the integrated purity that we see

        • from the fit to mES distribution

      • Not relevant from the point of view of the analysis but generated confusion during review

    Charged B sample

    Nominal Int. Pur. = 50 %

    F.Fabozzi


    Selection efficiencies

    e

    

    

    

    0

    00

     0

    e

    22.9%

    0

    0.6%

    0

    0

    0

    0

    

    0

    7.4%

    0.5%

    0

    0

    0

    0

    

    0.1%

    2.7%

    21.6%

    0.4%

    1.2%

    0

    0.1%

    

    0

    0

    0

    6.8%

    0

    0

    0

    0

    0

    0.3%

    1.0%

    0.1%

    6.6%

    0.8%

    0.6%

    Selection efficiencies

    • Total efficiency = 11.3%

    F.Fabozzi


    Selected events

    Selected events

    B   

    Wrong sign control sample

    B   

    Unblinded data candidates

    F.Fabozzi


    Background from generic b b

    1-prong lepton

    1-prong pi

    rho-channel

    Hadronic

    Vcb

    Hadronic

    Vcb

    Semilept.

    Vub

    Semilept.

    Vcb

    Semilept.

    Vcb

    Semilept.

    Vcb

    Background from generic B+B-

    • Mostly from semileptonic Vcb

      • Can we fight this background looking at the charged tracks list?

    3-prongs channel

    only 3 events selected

    2 semilept. Vcb

    1 hadronic charmless

    F.Fabozzi


    Systematic error i

    Systematic error (I)

    • Uncertainty on NBB

    • Uncertainty on signal selection efficiencies

      • Better understanding of systematics on neutrals

        • Now: smearing procedure on the photon energy

        • Cut on neutral energy in the EMC

          • Sensitive to the Data-MC agreement in the low-energy region (ex. simulation of machine background in the EMC)

      • Developing a module to study t+t- events

        • Tag one t (t-3prongs) and look the neutral energy distribution associated to the other t decay

          • Data-MC comparison

    F.Fabozzi


    Systematic error ii

    Systematic error (II)

    • Uncertainty on the expected background (bi) estimate

      • Peaking background: from generic B+B- MC

        • More generic MC is available  will reduce main systematic error

      • Continuum+combinatorial background: from data sideband

        • scaling to signal region based on the fitted Argus shape

        • Possible dependencies of the fitted Argus shape on the variables used in the selection taken into account

          • mES fits in bins of a given variable

            • a correction factor for each variable

        • Total correction as the product of the individual corrections

          • assumption that variables are uncorrelated

          • the effect of correlation between variables is small (total error on bi: 4.9  5.0, since the statistical error on bi is dominant

        • We may think to remove highly correlated variables if they do not add in signal-to-background discrimination

    F.Fabozzi


    Branching fraction extraction

    Branching fraction extraction

    • -2logQmin gives the statistical significance of the signal

      • ~ 0.1s significance

    • Likelihood ratio estimator Q

      • Value of branching fraction  Minimum of -2logQ

    Unphysical region

    +3.8

    Br(B-  t- n )= 1.1  10-4

    -1.1

    F.Fabozzi


    Upper limit determination

    Upper limit determination

    • Determination of C.L. and upper limit with a Toy MC

      • 10000 random experiments generated for 400 values of the branching fraction

    Br(B-  t -n )< 7.7  10-4 (90%C.L.)

    Uncertainty on

    bi included

    COMBINED with semi-lept analysis:

    Br(B-  t -n )< 4.1  10-4

    (90%C.L.)

    F.Fabozzi


    Expected sensitivity

    Expected sensitivity

    • For each channel we fluctuate the observed events according to a Poisson distribution

    F.Fabozzi


    Conclusions

    Conclusions

    • Conference paper will be made public soon

      • Some delay due to discussion on if/how to quote a central value

    • Another iteration of the analysis is starting

      • More signal and generic B+B- MC available

      • Better understanding of neutral systematics

      • Try to see if margins to improve background rejection

        • Semileptonic Vcb is the main source

    F.Fabozzi


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