A search for B ?? ? Recoiling Against B-?D0l-?X at babar

1
A search for B??? Recoiling Against B-?D0l-?X
at babar

• Stephen Jacob Sekula
• The University of Wisconsin-Madison
• Ph.D. Thesis Defense
• February 27, 2004

2
overview

• Theoretical Background and Motivation
• The PEP-II/BaBar B-Factory
• Tagging Events using Semi-Leptonic B-Decay
• Selecting B???
• Systematic Effects
• The Limit on the Branching Fraction
• Results and Conclusions

3
The Standard Model

• Matter is described by the interactions between
  quarks and leptons
• Their interactions are described by a symmetry
  group

electric charge
electric charge
LEPTONS
4
quark interactions

• The relationship between weak and mass quark
  eigenstates is given by the Cabibbo-Kobayashi-Mask
  awa (CKM) matrix
• Need to probe quark interactions cleanly

W
5
The rare decay B?l?
l
Pure leptonic decay, free of hadronic
uncertainties
W
B
?

• The Standard Model branching fraction is

helicity suppression
6
Theoretical Expectation
.

• StandardModelParameters
• Branching Fraction Expectations, by lepton flavor

7
Existing measurements

• Only limits have been set on the branching
  ratio
• My work is conducted at the Stanford Linear
  Accelerator Center's PEP-II/BaBar B-Factory.

8
pep-II

• PEP-II is an electron-positron storage
  ring/collider

PEP-II Specifications
Energy (HER/LER) 9.0/3.1 GeV Luminosity
(Instantaneous) 7.11033cm-2s-1 (250,000
BB/day)
9
The ?(4S) decay
Electrons (9.0 GeV) and Positrons (3.1 GeV)
collide so that EcmM?(4S) (10.58 GeV)
The first B-meson decays
B1
B2
The second B-meson decays
10
The babar detector

• Asymmetric Design
• Five Detector Sub-systems
• Silicon Vertex Tracker(SVT)
• Drift Chamber (DCH)
• DIRC (Cherenkov detector)
• Electromagnetic Calorimeter (EMC)
• Instrumented Flux Return (IFR)

11
Silicon Vertex Tracker (SVT)

• Double-sided detector modules, arranged in five
  layers
• Single-hit resolution 10-40?m (incidence
  angle-dependent)
• important for high-efficiency track
  reconstruction (97 efficient)

12
Drift chamber (DCH)

• Detects ionization of gas by charged particles
• Important for high-efficiency track
  reconstruction and vertexing
• Hit resolution0.1mm
• Energy loss overdistance (dE/dx)needed for
  particle ID (distinguishing kaons and pions by
  4? up to 1 GeV/c)

13
DIRC

• Detects Cherenkov radiation of incident charged
  particles
• velocity dependence of Cherenkov radiation allows
  for particle species separation
• gt90 kaon identi-fication efficiency (lt5 pion
  misidentification) up to 4 GeV/c (gt4?
  separation)

14
Electromagnetic calorimeter (EMC)

• Cs-I crystals arranged in a quasi-projective
  geometry
• 30 MeV resolution for1 GeV photon
• Essential ingredientin electron
  identification(90 electron efficiencywith 0.1
  pion mis-identification rate) formomenta above
  700 MeV

15
instrumented flux return (IFR)

• Steel interwoven with resistive-plate chambers
  (RPCs)
• RPCs detect ionization by incident particles
  (typically muons), or nuclear interactions
  by/decay of long-lived hadrons (i.e. K0L)
• Muon efficiency (? mis-id)2000 65 (5)2002
  40 (8)
• Muon efficiency is time-dependent due to loss
  in RPC efficiency

16
Data samples
Data used was collected between the summers of
1999 2002. NBB(88.9 1.0)106
Monte Carlo (MC) simulations are performed of the
major background species for this
analysis 3data for BB 1data for continuum
In figures and quoted yields, the MC is scaled to
the data luminosity
17
analysis overview

• The events of interest to this search contain one
  B meson decaying to ??
• This leaves one of the B-mesons unconstrained
  potentially large source of contamination
• Strategy
• First eliminate one of the B mesons by either
  completely or partially reconstructing it
• Constrain the remaining charged/neutral particles
  to be consistent with B???
• Extract the signal-content in the data using this
  technique

18
A Unique search strategy
Reconstruct one of the B-mesons in a
semi-leptonic, open charm decay mode (B-?D0l-?X)
tag B meson
B-

l
?
B
recoil or signal B meson
?
Search the recoiling particles for B???
This is a new approach to searching for this rare
decay.
?
19
Semi-leptonic B Decay

• Semi-Leptonic, Open Charm B-decays have
  inherently high branching fractions
• High-statistics tagged samplefrom which to start
  the search for B???
• The neutrino cannot be reconstructed. Particles
  other than the neutrino (X ?0,? from
  higher-mass D meson decay) can also be missing
• Higher levels of background than in
  fully-reconstructed events

20
B-?D0l-?X Event selection
.

• Global event restrictions
• at least one high-momentum lepton, zero event
  charge, and uniform distribution of
  charged/neutral particles in center-of-mass frame
  (characteristic of B decays)
• Selection of D0 mesons
• Decay modes D0?K-?, K-??0, K-??-?, KS0??-
  (KS0???-) this is 28.2 of the D0 branching
  fraction
• D0 mass within 3? of the fitted mean
• Selection of Dl Candidates
• Vertex pairs of leptons and D0 mesons, constrain
  them to be consistent with B decay

21
Dl Reconstruction

• Vertex D0s and leptons. Also vertex D0s and ?s
  to make Dl. These are collectively called Dl
  candidates
• Define the reconstructed angle between the Dl and
  the B that would have created it (in ?(4S) rest
  frame)(assumes only the neutrino is missing)
• -2.5 lt cos?B,Dl lt 1.1

22
D0l-?X Selection Efficiency

• The total tag efficiency in MC is
• factoring out the B-?D0l-?X and D0 branching
  fractions, the method is 15.8 efficient at
  selecting semi-leptonic decays (for the chosen D0
  decay modes).
• In 88.9 million BB events, the expected yield of
  semi-leptonic tags is 395,000
• After selecting events with one B-?D0l-?X, the
  tracks and neutrals remaining in each event (the
  recoil or signal side) are constrained.
• This sample is the basis for the search

23
data/MC efficiency
.

• Efficiency is compared in data/MC using
  double-tagged andsingle-tagged events
• Tag Efficiency is related to yield by

24
data/MC Efficiency (II)
.

• Assumption preselections affect both single and
  double tags in the same way (?1?2)
• The ratio N1/N2 cancels many terms
• The efficiency correction is then

25
Signal Side Selection

• The search proceeds by isolating the decay
  modes
• One track (signal track) left over after
  reconstructing B-?D0l-?X
• Must be either a muon or an electron and NOT a
  kaon
• originate from close to the ee- interaction
  point
• low momentum (plt1.2 GeV/c), consistent with a
  secondary decay
• little energy remaining in the calorimeter
• After making all these selections, extract the
  signal content from the data

26
Extra Neutral energy

• Main discriminating variable
• The neutral energy remaining in the
  calorimeterafter accounting for neutralenergy
  from the tag B meson

Only signal and background MC
Signal BR 110-4
27
Signal efficiency

• Total Monte Carlo signal efficiency
• factoring out the tag side efficiency and the
  ??l?? branching fraction, the efficiency is
  30
• The Standard Model predicts
• Monte Carlo background expectation
• Background events are dominated by semi-leptonic
  decays with missing charged particles or a K0L

28
extracting the signal content from data

• The Eextra distribution has distinct shapes for
  signal and background
• Develop models for the neutral energy
• Combine the models into a likelihood function
• Fit the data to determine the signal and
  background content

29
Signal model
two gaussians

• Signal eventssubdivide intotwo classes
• The signal model isa combination of the models
  for each subclass

Events with no physics
source of energy
30
Signal Model validation

• Use double-tagged eventsto create a sample with
  similarEextra properties

31
BackGRound model

• A 3rd order polynomial is used to model Eextra
  from background events
• Other models aredeveloped for latercross-checks
  of thefit
• Data extrapolation of the polynomial from
  0.5?0.0GeV in Eextra yields a background
  estimate of 119.812.5 (compared to 123.96.9 in
  MC)

32
Maximum likelihood fit

• The signal and background models (PDFs) are
  combined into a single maximum likelihood
  function
• ?s and ?b are the signal and background yields
  to be fitted in the data
• n is the total number of events in the data
• Fs(Eextra) and Fb(Eextra) are the signal and
  background shapes for Eextra

33
Likelihood fit checks

• Generate and fit toy MC with varying signal
  hypotheses
• Fit background-only MC samplesof size compatible
  with the data

0 input signallt?fittedgt0 ?fitted5
GEANT4 background-only MC
5 input signallt?fittedgt5
34
Data fit result

• The data is fit usingthe likelihood function
• Background yield (115.211.8) is in
  goodagreement with estimatesfrom background
  modelextrapolation (119.812.5) and MC
  (123.96.9)
• Signal yield (14.86.3) is 2? from zero
• 1.3? difference from Standard Model prediction
  (42)
• A limit will be set on the branching fraction

signalbackground
background
signal
35
Efficiency systematicuncertainties

• Several sources of systematic uncertainty enter
  into the efficiency estimate
• Semi-leptonic tag B reconstruction (double-tags)
• encapsulates all particle identification,
  tracking, etc. effects on the tag B meson
• Particle identification of signal track
• Neutral particle simulation (Eextra cut
  efficiency)
• Tracking Efficiency (probability of
  reconstructing signal track)
• The systematic uncertainty will propagate into
  the limit-setting procedure

36
Particle id efficiency (signal track)
.

• Particle ID (PID) efficiency is not modeled
  perfectly in MC
• PID is studied in data/MC as a function of
  species
• PID Tables provide ?data/?MC as a function of
  momentum and angle ? track-by-track weight to
  correct efficiency
• BaBar uses these PID Tables to correct efficiency
  in the MC, but the tables do not take account of
  correlations between particle selection criteria
• I construct custom PID tables combining the
  lepton acceptance and kaon veto used on the
  signal track

37
Particle id efficiency (II)
.

• I compare the correction from the custom PID
  tables to those originally used to correct the
  MC
• Weighting the corrections by contribution to the
  signal MC yields the total PID correction

MUONS
ELECTRONS
Low Momentum, Low Efficiency
38
Total systematic uncertainty

• The corrected signal efficiency is

39
Limit setting procedure

• The limit setting procedure is based on the CLs
  Method (a modified frequentist approach)
• Define an estimator, Q
• Determine the value of Q for the result (Qdata)
• Determine the valeue of Q for a given
  signalbackground (background only) hypothesis
  Qsb(Qb)
• The confidence level (CL) of a signal hypothesis,
  s, is defined as

40
Limit setting (II)

• The fitted signal yield, ?sfitted, is the chosen
  estimator
• Determine the distribution of fitted signal
  yields, D(?sfittedsb), from toy MC
• Count the number of experiments below a given
  point
• The point ?s90 is the signal expectation that
  corresponds to the 90 confidence level
  (CLs1-0.9) for a given s

41
Nominal sensitivity

• The sensitivity is the expected limit when no
  signal is present
• The branching fraction sensitivity is then
• This is the most sensitive search in the world

Null Signal Hypothesis
zero signal
data result(?s9024.2)
data result
42
The impact of Shape systematic effects

• Several factors could affect the limit in data
• The background shape may be differerent
• Different true parameterization
• Unmodeled feature of the shape (i.e. a
  signal-like feature)
• The signal shape may be different
• Study the effect of such possibilities and choose
  the most conservative option (i.e. use nominal
  models, add signal-like features to the
  background model, etc)
• observe effects on the limit in the
  background-only GEANT4 MC samples

43
summary of shape study
Change the background parameterization No Effect
on Limit
Change the background shape according to data/MC
agreement in several control samples Nominal
Choice of Shape is most Conservative
Change signal shape within uncertainty No Effect
on Limit
The nominal PDF parameterizations are used to
fit the data and set the limit
44
Including Efficiency Systematics in the limit

• Include the 9.9 uncertainty on the signal
  efficiency and 1.1 uncertainty on the B-meson
  count in the limit
• Including systematics in the limit
  calculation,I obtain the dotted curve
• The data result ishigher than the
  expectedresult when no signal is present

Nominal limit including systematics is 2.710-4
Nominal limit without systematics is 2.310-4
data result
45
Data branching fraction limit

• The limit obtained from the fitted signal yield
  in data (14.86.3)
• After including efficiency systematic
  uncertainty
• The limit is worse than that expected if there's
  no signal
• The background-only assumption may not completely
  apply because the Standard Model does predict
  signal events (of order the fit uncertainty)
• The signal yield is 2? from zero but 1.3? from
  the Standard Model

46
summary

• A search for B??? has been conducted at the
  PEP-II/BaBar B-Factory
• One B-meson is reconstructed as B-?D0l-?X
• Search the recoiling particles for B???
• A signal yield in data (14.86.3) is obtained
  from a maximum likelihood fit
• A limit is set on the branching fraction

47
outlook

• The B-Factory program will collect 500 fb-1 by
  2006
• the current BaBar dataset totals 170 fb-1 and
  will exceed 200 fb-1 by summer
• the KEK-B/Belle B-Factory has not reported a
  result for B???, but its data sample will
  exceed 250 fb-1 by summer
• Currently, this analysis is sensitive to Standard
  Model effects. By 2006, it will reach 5?
  (statistical) discovery potential
• A discrepancy with the Standard Model could
  indicate new physics

current
(5?)
2006
48
Backup slides
49
The importance of B?l?
.
.

• Allowed by the Standard Model but not yet
  observed
• Pure leptonic decay no uncertainty from QCD
• In conjunction with measurements of Vub, offers
  a chance to measure fB
• fB is known only from lattice QCD and is a
  critical ingredient in the theory of B meson
  flavor mixing
• Potentially sensitive to new physics that might
  enhance or suppress the decay
• charged Higgs exchange (SUSY)

50
B-mixing
.
B mixing frequency
parameters and QCD terms
Use the Wolfenstein approximation to relate the
ratio to other constraints on the CKM unitarity
triangle
51
supersymmetry
.

• The Minimal SuperSymmetric extension of the
  Standard Model (MSSM) requires two Higgs doublets
• one gives mass to up-type quarks, the other to
  down-type
• The introduction of a charged Higgs boson
  corrects the branching fraction
• A branching fraction measurement can set limits
  on tan? and the charged Higgs mass

52
B????
.

• Inherently not a significant background source
• Eextra cut is a powerful rejection factor in this
  analysis

53
Particle ID Efficiency by selector and year
.
ELECTRONS
MUONS

• The Particle Identification can change slightly
  over time with changing detector conditions.
• Muon identification is particularly affected by
  the degrading performance of the RPCs in the IFR

54
Global event selection
.

• Select events with at least one high-mometum
  lepton
• pl gt 1.0 GeV/c (l is either an electron or a
  muon)
• Require events to be well reconstructed
• Net event charge is zero
• Events are consistent with the target class
• Total number of charged particles 10
• Large missing mass (Mmissinggt1.0GeV/c2)
• Events are not collimated (R2alllt0.9) particles
  BB decay tend to be uniformly distributed in the
  ?(4S) frame

55
D0 Reconstruction
.

• D0 mesons are reconstructed in the modes
  D0?K-?,K-??0, K-??-?, KS0??- (KS0???-)
• Requirements
• daughters converge at a common point
  (vertex)
• reconstructed D0 has mass between
  1.81-1.91 GeV/c2 reconstructed D0 has momentum
  0.5 lt p lt 2.5 GeV/c

reconstructed D0 mass within 3? of the fitted
mean
K-??0
56
treatment of multiple Dl candidates

• An event typically has multiple Dl candidates
• A best candidate is selected using the D0 mass
• mass probability density functions (PDFs) are
  generated from reconstructed Dl candidates that
  match real ones in simulation
• If the best candidate isa Dl, the event is
  vetoed

57
Double tag method
.

• Use only the double-tagged events to compare
  data/MC
• assumptions
• the efficiency of selecting each semi-leptonic B
  decay is the same
• the double tags are dominated by BB- (2 from
  B0B0 in MC)
• Ratio of efficiencies in data and MC
• Compatible with single/double-tag method

58
Signal Side Selection
.

• The search proceeds by isolating the decay
  modes
• One track (signal track) left over after
  reconstructing B-?D0l-?X
• The charged track must be either a muon or an
  electron and NOT a kaon
• The track must originate from close to the ee-
  interaction point

59
Continuum rejection
.

• Removing continuumbackground relies onevent
  topology
• Use angle between Dl thrust and signal track to
  reject collimated events

Reject ee-???- using the minimum invariant
mass calculable from any three tracks in an
event
60
Signal track momentum
.

• The signal track from B??? is the result of a
  secondary decay (typically low momentum)

electron
muon
(Note for these plots, only events with lots of
extra energy in the calorimeter are retained.
This keeps background and removes signal)
61
Energy and momentum
.

• Look at Eextra for high (gt1.2 GeV/c) and low
  (lt1.2 GeV/c) momentum signal tracks

psignallt1.2 GeV/c
psignalgt1.2 GeV/c

• Notes on the disagreement
• not associated with any one (set of) MC
  background species
• Disagreement lives in psignalgt1.2 GeV/c (70)
• Signal MC contains only 30 of signal events
  above 1.2 GeV/c

62
Neutral simulation
63
Tracking efficiency

• BaBar has also studied track reconstruction
  efficiency as a function of momentum and angle
• Weights relating efficiency in data/MC
• Applying these weights to the signal track yields
  the efficiency correction
• The correction has an uncertainty of 0.008 per
  track

64
Cls example 90 CL
This is repeated for many signal hypotheses to
determine the relationship between ?sfitted and
?s90
fitted
fitted
65
Effect of a background estimate discrepancy

• Background Estimate(s)
• 119.812.5 (Data Extrapolation)
• 123.96.9 (Monte Carlo)
• Use Limit Curve on theGEANT4 experiments

Variation in the nominal upper limit is less than
one event Negligible effect
66
Uncertainty on the background shape

• Potentially more serious concern
• Need a data control sample that mimics the Eextra
  shape that expected from the background
• Unable to isolate such a sample
• Do the next best thing develop samples related
  to the signal topology and compare data/MC
  agreement
• Use bin-by-bin agreement as a correction function
  to the nominal background PDF
• Generate toy MC with the new PDF, fit the toy MC
  with the original (nominal) PDF and observe the
  effect on the CL curve

67
background shape (II)

• Control samples for data/MC agreement study

High-statistics test of the Eextra agreement
Wrong-charge sample, close to signal sample, low
statistics
Neutral B-meson sample, wrong charge, very low
statistics
68
background shape III
Eextra
Data/MC Comparison
MC Fit
ExampleCheck the effect by applying the
derived CL curve on the GEANT4 experiments
Data Fit
Data/MC Fit Ratio (Correction Function)
The nominal background model is more conservative
and is kept
69
Uncertainty on the signal shape

• Change mean of peak gaussian at 0.2 GeV
• based off the uncertainty from the double-tag
  model validation
• shift the mean by 0.015 GeV
• No significant effect