Note: This result has been superseded by an updated analysis with 1.9 fb–1 of data.
[Public Web Page of Updated Analysis]

Search for the Flavor Changing Neutral Current Decay t→Zq
Analysis Public Web Page

Charles Plager, David Saltzberg, Michael Sutherland (UCLA)
Melissa Franklin, Ingyin Zaw (Harvard)
Jennifer Lindsay Gimmell (Rochester)
Ulrich Husemann, Paul Tipton (Yale)



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The above plot shows the mass χ² distribution for both the tagged and anti-tagged FCNC selections. Data are overlaid with both the total background and the FCNC signal predictions at the measured 95% C.L. upper limit and shown with total systematic uncertainties. The tagged and anti-tagged selections are analyzed separately. The signal regions for the two selections are to the left of the green arrows. The main sensitivity of this search lies with the events in the tagged selection with low χ².

  • Abstract
  • Event Selection
  • Results
  • Background Estimates
  • Limit Calculation
  • Systematic Uncertainties
  • Method Validation
  • Documentation
  • Abstract

    In the standard model, top quark flavor changing neutral current (FCNC) decays are highly suppressed; they do not occur at tree level and are only allowed at the level of quantum loop corrections, but at very small branching fractions on the order of 10-14. Some exotic physics models including SUSY and two Higgs doublet models predict higher branching ratios, up to 10-4. Any signal of this rare decay at the Tevatron would be an indication of new physics.

    Previous searches for the top FCNC decay t→Zq have been performed in CDF Run I and by the LEP experiments. The Run I analysis yielded an upper limit on the branching fraction of t→Zq of 33% at 95% C.L. The current best 95% C.L. upper limit on the t→Zq branching fraction is 13.7%, inferred from the L3 experiment's non-observation of e+e-→ tq.

    We perform a blinded search for the flavor changing neutral current decay of the top quark t→Zq with 1.12 fb-1 of data recorded with the CDF-II detector. Using Z+≥4 jet candidate events both with and without a loose secondary vertex b-tag, we observe a data yields consistent with the background expectations. We set a 95% C.L. upper limit on the branching fraction B(t → Zq) of 10.4%; the expected upper limit was 6.8%

    Event Selection

    We perform a blind search for the FCNC decay t→Zq, with the blinded region defined as events with a reconstructed Z in the mass range of 76-106 GeV/c², four or more jets, and mass χ² (constructed from reconstructed W, SM top, and FCNC top masses) less than 9. For the Z reconstruction, we require exactly one lepton (electron or muon) pair of the same flavor and opposite charge. One of the leptons that form the Z can must be identified by tight lepton identification criteria, the other one can be an isolated track in the drift chamber. In addition to the Z candidate, we require four or more jets with transverse energies ET > 15 GeV.

    We split the data sample into two subsamples that are analyzed separately, one which requires at least one jet to be identified as a b-jet by the loose flavor of the standard CDF secondary vertex b-tagging algorithm, and one which is anti-tagged. Using two signal regions instead of a single selection improves the sensitivity of this analysis. We take the signal acceptances and trigger efficiencies from a Monte Carlo simulation with appropriate corrections for the simulation's deficiencies applied. We normalize the expected event yield to the measured top anti-top cross section in the lepton+jets channel. More information about this cross section measurement can be found in CDF Note 8795.

    There are several physics processes that have signatures consistent with our event selection. The dominant background contribution for this analysis comes from Z bosons produced in association with jets (Z+jets), which we estimate using a combination of data and Monte Carlo techniques. Another background contribution comes from standard model top production, tt→WbWb events in which the invariant mass of two leptons in the dilepton decay mode or a lepton and a jet misidentified as a lepton in the lepton+jets decay mode fall within the Z mass window. A contribution similar in size comes from dibosons which have a real Z in the event (WZ and ZZ). The SM top and diboson backgrounds are estimated using Monte Carlo simulations. We found background contributions from WW diboson and W+jets production negligible; these processes do not contain a real Z in the final state.

    The predicted background contributions are summarized in the table below.


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    Results

    We optimize the event selection for the best expected limit, i.e. the weighted average of limits, where the weight is the Poisson probability for the number of events assuming background only. The optimization is performed using additional cuts on the transverse momenta of the four leading jets, the transverse mass, and the mass χ² in addition to the base selection. After the optimization and relative to the base cuts, 71% (56%) of the tagged (anti-tagged) signal remain as compared to 16% (7%) of the background. The optimized event selection criteria are given in the table below. The best expected limit using a Feldman-Cousins method with systematic uncertainties is 6.8%±3.0%. Below we show the list of optimized cuts:


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    After optimizing the event selection and determining the systematic uncertainties (see below) for the final selection criteria, we remove the mass χ² veto (allowing events with √χ² < 3.0) to unblind the signal region. The data yields are shown in the table below:


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    The data in the signal region is consistent with the expected background. We apply a Feldman-Cousins limit calculation for the two signal regions including the full systematic uncertainties. We set a 95% C.L. upper limit on the t→Zq branching fraction, BR(t→Zq) < 10.4, consistent with the expected limit of 6.8%± 3.0%. The above limit has been obtained using a top mass of 175 GeV/c². When we assume a top mass of 170 GeV/c², we obtain BR(t→Zq) < 11.0% at 95% C.L.

    The plot below shows the mass χ² distributions for the tagged and anti-tagged data samples. The data points are compared to the the background prediction and the expected FCNC yield at the observed 95% C.L. upper limit for the t→Zc branching fraction of 10.4%. The data in the signal region are consistent with the expected background. The green arrows indicate χ² cuts that define the signal regions.


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    Background Estimates

    The χ² Discriminator

    Handling the Z+jets background is a very important part of this analysis; the strongest discriminator between the FCNC signal and Z+jets background is a mass χ² constructed using the reconstructed W, SM top, and FCNC top masses:


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    In the above equation, we assume a top mass of 175 GeV/c² and the resolutions σ(W,rec)=15 GeV, σ(t→Wb)=24 GeV, and σ(t→Zq)=21 GeV. We evaluate χ² for all permutations of the leading four jets in the event and select the permutation with the lowest χ².

    Shown below is the discriminating power of the mass χ²: the χ² distribution for the FCNC signal is well separated from the χ² distribution of the background. All background distributions are similar in shape.


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    Z+jets Background & Tagging Rate

    Below, we show the pre-tagged and the b-tagged jet multiplicity spectra for both data and MC. The left plot shows the the jet multiplicity spectra, and the right plot contains the data to MC ratio. In the plot of the ratio of data to MC simulation for the b-tagged case, we mark with a red line to emphasize the average underestimation of heavy flavor tagging in the ALPGEN generated Monte Carlo samples by nearly 30%. Both the pre-tagged n-jet spectrum and the tagged n-jet spectrum below are normalized to the zero-jet bin of the pre-tagged n-jet distribution.


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    With knowledge that the ALPGEN generated MC samples shows a consistent under-estimation of extra jet production, we use additional techniques to estimate the tagged Z+jets background. In order to take a more data-driven approach to the Z+jets background estimation, we use tail of the mass χ² distribution (see below). Using a cut on √χ² > 3, the vertical line in the plot below, allows us to decrease the blinded region in order to look at events with 4 or more jets, as long as √χ² > 3 for that event. Using this information, we are able to approximate the total background contribution present in our signal region by fitting the total background high χ² tail to data. To get an estimate of the uncertainty of this method, we compare the cuts made at √χ² > 3 and at √χ² > 3.2. We average the two results to predict 130±28 total pre-tagged background events.


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    Using the b-tagging information from these high χ² tail events along with tagging templates, we estimate the event tagging rate to be 15% ± 4%. We estimate the tagged Z+jets background to be 17.6±6 events.

    Limit Calculation

    We use both Feldman-Cousins (FC) and Bayesian frameworks for calculating expected limits; both frameworks take systematic uncertainties into account in the limit calculation. We find that the limits obtained in either framework track each other well. As the limit calculation in the FC framework is very CPU-intensive, we optimize our selection criteria for the best expected limit using the Bayesian framework and use the FC framework to obtain the final limit for our signal event yields.

    Using the Bayesian framework, we calculate the expected limit for several different scenarios which account for miscalculations of the background contributions and the event b-tagging rate. Note that the optimal point remains unchanged for several different background and event b-tagging rate scenarios.


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    The event selection for the b-tagged and the anti-b-tagged data samples are optimized for the best combined expected limit. We normalize to the measured lepton+jets top cross section that requires two or more secondary vertex b-tags, while accounting for the inclusion of top's FCNC decay. This choice minimizes the ratio of the FCNC to the SM lepton+jets acceptance at 32%. The single b-tagged cross section measurement has a selection similar to the FCNC selection, which would help reducing the systematic uncertainties of our measurement; however, the overlap in acceptance because of the similarities results in a larger acceptance ratio (45%) and a poorer expected limit on the t→Zq branching fraction.


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    Systematic Uncertainties

    Signal Systematic Uncertainties

    From our normalization to the measured lepton+jets cross section, we attribute systematic uncertainties for the evaluated ratio of the FCNC acceptance to the lepton+jets acceptance, distinguishing between correlated and anti-correlated uncertainties.

    Uncertainties which we label as correlated are those which shift both the anti-tagged and the tagged selections in the same direction. We attribute correlated systematic uncertainties to Monte Carlo corrections factors (lepton scale factors for lepton identification efficiencies and separate trigger efficiencies), the correction on the jet energy as identified by the calorimeter, and estimations on initial state radiation (ISR) and final state radiation (FSR) from the event. Since our signal Monte Carlo sample is generated flat in cos(θ*), the angle between the top boost and the positive lepton in the Z rest frame, we must re-weight it to the appropriate handedness: 65% longitudinal, 35% left-handed. We apply a systematic uncertainty on this helicity re-weighting of the signal FCNC Monte Carlo sample. We also include a correlated systematic uncertainty on the parton distribution functions.

    Systematic uncertainties that are anti-correlated shift the anti-tagged and tagged selections in opposite directions. We attribute an anti-correlated systematic uncertainty on the b-tagging scale factors applied to the Monte Carlo simulation. We also include an anti-correlated systematic uncertainty for the difference in event tagging rate between Zu Wb and Zc Wb final states.

    We list our correlated and anti-correlated systematic uncertainties below:


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    Background Systematic Uncertainties

    Our background systematic uncertainties are dominated by the yield of Z+jets events in the signal region and by the event b-tagging rate for the Z+jets events. These systematic uncertainties are on the ratio of events in the χ² tail to the signal region, which is used to predict the background in the signal region. Below we list the additional systematic uncertainties associated with the background to our signal region.


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    The main contribution to the background systematic uncertainties comes from varying parameters in the ALPGEN MC generator. The choice of ALPGEN parameters also influences the jet multiplicity spectra. Here we show the data-MC comparison of the jet multiplicity spectrum in events with a reconstructed Z. On the left, the jet multiplicity spectrum, and on the right the ratio of data over MC prediction. The background prediction contains Z+jets production, SM top production, and diboson production. The colored band represents the standard deviation of the uncertainties expected from varying the ALPGEN parameters.


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    Method Validation

    Track Momentum Correction for Bremsstrahlung Recovery

    We use the track momentum correction for Zs formed from tight central electrons and isolated tracks. Note that this correction gives a much narrower Z peak and therefore increases the acceptance of dielectron Zs by almost 3%. The correction is well modeled in the MC simulation. Note that we have used an inclusive Z→ee MC sample generated by PYTHIA for this study.

    (a) Comparison of Z invariant mass in data before and after correction.


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    (b) Comparison of Z invariant mass in the MC simulation before and after correction.


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    (c) Comparison of data and MC simulation before correction.


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    (d) Comparison of data and MC simulation after correction.


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    Kinematics for Three-Jet Events

    Below, we show data to MC comparisons for the kinematic distributions of 3-jet event samples. As the ≥4 jet bin is blinded, we use 3-jet events as our control samples to validate the event kinematics. The Monte Carlo simulation models the data well.

    Jet Transverse Energy of the Three Leading Jets

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    Transverse Mass

    The transverse mass of the event is constructed from the three jets and the reconstructed Z:

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    χ² Distributions

    The full mass χ² requires four jets and therefore cannot be validated in Z+3 jet events; however, a W boson candidate mass and one top candidate mass can be reconstructed. On the left, the mass χ² based on W→qq and t→Wb. On the right, the mass χ² based on W→qq and t→Zc. In both plots, we assume that two of the three jets come from the W decay. Third jet is the b-jet (left) or the c-jet (right).



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    Kinematics for Anti-Tagged Events

    Z Invariant Mass

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    Jet Transverse Energy

    Transverse energy distributions of the four leading jets for the anti-tagged selection. The expected backgrounds are normalized to the data event yield. All distributions are N-1 distributions, i.e. all selection cuts are applied except for the kinematic variable shown.


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    χ² Distribution and Transverse Mass

    The χ² distribution for the anti-tagged events is constructed with the reconstructed W boson, SM top and FCNC masses as shown in the formula:


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    The transverse mass of the event is constructed from the four leading jets and the reconstructed Z:


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    GT and Missing Transverse Energy

    As our signal does not contain any real missing transverse energy, we prefer to show the quantity GT instead of the more common HT. GT is defined as the scalar sum of the lepton transverse momenta and jet transverse energies (without missing ET). Both the missing ET and GT distributions for the anti-tagged sample are consistent with the background prediction. The expected backgrounds are normalized to the data event yield.


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    Kinematics for B-Tagged Events

    Z Invariant Mass

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    Jet Transverse Energy

    Transverse energy distributions of the four leading jets for the anti-tagged selection. The expected backgrounds are normalized to the data event yield. All distributions are N-1 distributions, i.e. all selection cuts are applied except for the kinematic variable shown.


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    χ² Distribution and Transverse Mass

    The χ² distribution for the b-tagged events is constructed with the reconstructed W boson, SM top and FCNC masses as shown in the formula:


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    The transverse mass of the event is constructed from the four leading jets and the reconstructed Z:


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    GT and Missing Transverse Energy

    As our signal does not contain any real missing transverse energy, we prefer to show the quantity GT instead of the more common HT. GT is defined as the scalar sum of the lepton transverse momenta and jet transverse energies (without missing ET). Both the missing ET and GT distributions for the b-tagged sample are consistent with the background prediction. The expected backgrounds are normalized to the data event yield.


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    Documentation

    Public Analysis Note: CDF Note 8888.
    Example Slides: [ppt] [key.tgz]

    Last modified: Mon Feb 18 16:32:12 CST 2008 by Ulrich Husemann