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Study of the heavy flavor content of jets produced in association with W bosons in pp collisions at

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Title: Study of the heavy flavor content of jets produced in association with W bosons in pp collisions at


1
Study of the heavy flavor content of jets
produced in association with W bosonsin pp
collisions at ? s1.8 TeV.
Fermilab CDF Paper Seminar, September 2001
2
Introduction
  • Production of W bosons in association with jets
    has been the subject of many studies at CDF
  • production studies
  • QCD studies
  • Previously all the excesses of tagged W ?3 jets
    events have been attributed to tt production and
    used to measure 1
  • SECVTX
  • JPB
  • SLT
  • This Study
  • Use theoretical value of
  • Test if Standard Model prediction is compatible
    with the observed yield of different tags as a
    function of jet multiplicity.
  • 1 Phys. Rev. D64032002, 2001

3
Data sample lepton jet events
  • Kinematic cuts
  • One high Et lepton (e , m)
  • Et gt20 GeV
  • Central ( h lt1.1)
  • Isolated
  • Et gt 20 GeV
  • at least 1 jet with Et gt15 GeV and h lt2.0
  • b-identification
    Efficiency
  • SECondary VerTeX (SECVTX) 50
  • Jet-ProBability (JPB)
    50
  • semileptonic decay (SoftLeptonTagging )
    20




4
W jets composition
Source Phys. Rev. D64032002, 2001
5
W jets after tagging
Source Phys. Rev. D64032002, 2001
6
Various topological channels
7
Correlation between SLT and SECVTX tags
  • Begin study selecting Wjet events with both
    SECVTX and SLT tags
  • Observations
  • the numbers of events with SLT and no SECVTX
    tags (mostly fakes) are consistent with the
    prediction.
  • The numbers of observed and predicted events
    with multitags, which are mostly contributed by
    real heavy flavor, are not very consistent with
    the prediction.
  • Since 70 of the events tagged by SECVTX contain
    b-jets (20 c-jets) we check the semileptonic BR
    in heavy quark jets tagged by SECVTX.

8
Events with and w/o SECVTX and SLT tags in the
same jet (superjet)
SECVTX Only Events (Complementary Sample)
Superjet Events
  • the complementary sample has 43 events and 43.6
    3.3 are expected
  • In the superjet sample, the probability of
    consistency with the SM in the 4 jet bins is
    0.65.
  • the a posteriori probability of observing 13 or
    more events with a superjet when 4.4 0.6 are
    expected in the W2,3 jets bin is P10-3

9
Additional SECVTX tags
  • If superjet events were Complementary
    Sample-like, we would expect 1.8 DT.
  • The likelihood of observing 5 additional SECVTX
    tags is 4.1x10-2.

10
Cross-check using generic-jet data
  • Herwig simulation
  • tuned by fitting the rates of SECVTX and JPB tags
    in Phys. Rev. D64032002, 2001

11
Cross-check using generic-jet data
  • The efficiency for finding supertags in the data
    is smaller than in the simulation (855)
  • note after tagging with SECVTX, the heavy
    flavor composition of generic-jets is similar to
    W jet events

12
Study of Kinematics
  • If the 13 events are a statistical fluctuation,
    the kinematics of this sample will be
    consistent with the S.M. simulation and the C.S.
  • We chose two sets of 9 variables to look for
    differences
  • First set
  • studies d2s / (dpT dh) for every different
    object in the final state (8 var.)
  • Replace ET with the system lsujb
  • Add the angle between the lepton and W (check if
    leptons are consistent with the decay of W
    bosons)
  • This set of 9 variables fully describes the
    kinematics of the final state with modest
    correlations.

13
Study of Kinematics (cont.)
  • Each data distribution for a given variable xi is
    compared to the sum of various SM contributions
    using a K-S test.
  • Use the Kuipers definition of distance between
    the cumulative distributions of data and SM
    prediction
  • With Monte-Carlo pseudo experiments, randomly
    generate contributions of the various parent
    distributions.
  • Determine the probability distribution of the K-S
    distance , Wi(d).
  • Define the probability Pi that the xi
    distribution in the data is consistent with the
    SM simulation as

14
Primary lepton
15
Primary lepton
16
Superjet
17
Superjet
18
Additional jets (b-jets)
19
Additional jets (b-jets)
20
ET or ETlbsuj
21
Longitudinal E or y lbsuj
22
Azimuthal Angle (l,E)
23
Event Vertex
  • The binomial probability that the vertex
    asymmetry and the h-asymmetries are due to a
    equal or larger statistical fluctuation is
    P1.610-4 (3.8 s)
  • we know of no such physics process
  • It may be a low probability statistical
    fluctuation
  • obscure detector effect such asymmetries are
    not visible in any other control sample

24
Summary of the Probabilities
25
Additional probabilities
  • Second Set
  • The first set of 9 kinematic variables were part
    of a larger set of 18 variables used to study the
    events.
  • The remaining 9 variables study the combination
    of the various objects in the superjet events

26
18 Kinematical variables
lt Pgt0.50 RMS0.24
lt Pgt0.13 RMS0.11
27
Superjet Event propertiesPrimary Lepton
distributions
Superjet Sample
Superjet Sample
Complementary Sample
Complementary Sample
28
Superjet Event propertiesSoft Lepton
distributions
  • Soft leptons
  • not prompt
  • in 8 cases are part of the SECVTX tag
  • emitted along the superjet axis

29
Superjet propertiesLifetime
  • Lifetime
  • pseudo-t
  • The pseudo-t distribution does not account for
    neutral particles emitted in the heavy flavor
    decays (boost factor 1.1 in b and c decays)

30
Superjet propertiesLifetime
  • tip
  • define Rt tip/pseudo-t
  • compare data to the SM simulation with a K-S
    test
  • control sample P0.35
  • b-jets P0.47
  • superjets P0.05

31
Check of Rt with generic-jet data
Data
Herwig
32
Superjet PropertiesSECVTX tag Invariant Mass
33
Superjet propertiesSoft Lepton pT
  • Compare to a SM simulation, in which the superjet
    transverse momentum distribution in each SM
    process has been sculpted to reproduce the data
  • the usual K-S test yields P 910-4

34
Check of the fragmentation (control sample)
Complementary sample
13 events
35
Check of the fragmentation (generic-jet data)
  • 550,000 generic-jet events in the data and in
    the Herwig simulation (JET20, JET50, and
    JET100).
  • 1324 supertags in the data
  • 1342 simulated supertags

36
Studies of the primary lepton
0.1 lt I lt0.2
37
Studies of the primary lepton
  • The pT of the primary leptons has a cluster at
    the 20 GeV/c threshold
  • lower the theshold in the high-pT lepton sample
    to 18 GeV/c
  • No events with a superjet found
  • Search in the low-pT lepton sample with a 10
    GeV/c threshold
  • This sample is prescaled by a factor of about
    1.3
  • Smaller integrated luminosity (Run 1B only) it
    corresponds to 8 of the 13 events with a superjet
  • We find 6 of the 8 events, and an additional one
    containing a primary lepton with pT17.7 GeV/c

38
Primary lepton trigger studies
  • We require that the primary leptons fired the L2
    trigger
  • For muons the L2 trigger efficiency is about 70
  • If superjets are real, we expect to have lost 2
    muon events with a superjet because of this
    request
  • as seen before, 85 of the superjets contain a
    soft muon above the L2 trigger theshold
  • we then expect to recover 1 or 2 W2,3 jet
    events with a supertag removing the L2 request
  • If superjet events are a fluctuation of SM
    processes, we expect to recover
  • 3 W1 jet, 1.1 W2,3 jet events (all without
    supertags)
  • We find 3 W1 jet events with no supertags, and
    1 W2 jet 1 W3 jet events (both
    with a supertag)

39
Plug electrons
40
Conclusions
  • Comparison of observed rates of SECVTX and SLT
    tags with SM predictions, including NLO
    calculation of single and pair produced top
    quarks are generally in good agreement.
  • However, we find an excess of events which have
    jets with both SECVTX and SLT heavy flavor tags.
  • Detailed examinations of these events find them
    difficult to reconcile with a simulation of SM
    processes, which well reproduces closely related
    data samples.
  • Extensive studies of these events and
    investigations of larger samples of generic jet
    data have not revealed any effect which indicates
    the existence of detector problem or simulation
    deficiencies.

41
Prospects for Run II
  • Run II will have the potential to confirm these
    observations and allow a highly detailed study of
    this class of events.
  • Run II Advantage
  • All probability measurement will be performed a
    priori thanks to the pioneering work performed
    on the Run I data.
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