Evidence for Single Top Quark Production in L=1.51 fb-1
|Florencia Canelli (FNAL), Peter Dong (UCLA), Bernd Stelzer (UCLA), Rainer Wallny (UCLA)|
We present a search for electroweak single top quark production using
1.51 fb-1 of CDF II data collected between February 2002 and
January 2007 at the Tevatron in proton-antiproton collisions at a
center-of-mass energy of 1.96 TeV. The analysis employs a matrix-element
technique which calculates event probability densities for signal and
background hypotheses. We combine the probabilities to form a
discriminant variable which is evaluated for signal and background
Monte Carlo events. The resulting template distributions are fit to the data using a binned likelihood approach. We search for a combined single top s-
and t-channel signal and measure a cross section of
3.0+1.2-1.1pb, assuming a top quark mass of 175
GeV/c2. The probability that the observed excess originated
from a background fluctuation (p-value) is 0.09% and the expected (median)
p-value in pseudo-experiments is 0.13%. We use the cross section measurement
to directly determine the CKM matrix element Vtb and measure
This analysis uses events from leptonic decay of the W boson.
We require a single, well isolated high-transverse-energy lepton,
large missing transverse energy (from the neutrino), and exactly two
high-transverse-energy jets. Of these jets, we require at least one
to be identified as originating from a b-quark by secondary
vertex tagging. The secondary vertex tag identifies tracks associated
with the jet originating from a vertex displaced from the primary
vertex. We further require the missing transverse energy and the jets
not to be collinear for low values of missing transverse energy. This
requirement removes a large fraction of the non-W background
while retaining most of the signal.
Jet multiplicity distribution for signal and background processes. We compare the predicted number of events in each W+jet bin to the number of events observed in data. Uncertainty on the data are statistical; the hatch marks represent systematic errors in the background estimate.
This analysis is based on a Matrix-Element method in order to maximize
the use of information in the events [2,3]. We calculate event probability densities
under the signal and background hypotheses as follows. Given a set of
measured variables of each event (the 4-vectors of the lepton and the
two jets), we calculate the probability densities that these variables
could result from a given underlying interaction (signal and
background). The probability is constructed by integrating over the
parton-level differential cross-section, which includes the matrix
element for the process, the parton distribution functions, and the
detector resolutions. This analysis calculates probabilities for four
different underlying processes: s-channel,
t-channel, Wbb-bar, Wcc-bar, and Wc+jet.
To quantify the single top content in the data, we perform a binned maximum likelihood fit. We fit a linear combination of signal and background shapes of the event probability discriminant to the data. The background normalization are Gaussian constraint in the fit. The fit determines the most probable value of the single-top cross section. All sources of systematic uncertainty are included as nuisance parameters in the likelihood function. Sources of systematic uncertainties can affect the normalization and shape for a given process. Correlations between both are taken into account through a common nuisance parameter (&deltai).
Validation of the Method
Several tests have been performed for this analysis.
We compare the distribution of many kinematic variables
predicted by Monte Carlo simulation for signal and
background to the data. In particular, we compare the distributions
of the input variables to
ensure the data matches the Monte Carlo prediction. We evaluate the event probability
discriminant in the untagged W + 2 jets sample, a high-statistics control sample with
very little single-top content (<0.5%). We also evaluate the
event probability discriminant
in the tagged dipleton + 2 jets sample (using only the most energetic lepton) and in tagged lepton + 4 jets sample (using only the two most energetic jets
as input to the discriminant), which
should agree well with tt-bar Monte Carlo. In all control samples, the data agrees well with the Monte Carlo prediction.
Evaluation of the event probability discriminant in the high statistics taggable but untagged W + 2 jets control sample.
The discriminants evaluated in the tagged dilepton + 2jets sample (0.95 fb-1 sample) and the tagged lepton + 4jets sample (1.51 fb-1), both of which are mostly composed of tt-bar events.
Each source of systematic uncertainty can posses a normalization uncertainty and a shape
uncertainty. The normalization uncertainty includes changes to the
event yield due to the systematic effect, and the shape uncertainty
includes changes to the template histograms. Both of these effects are
included in the likelihood function as shown above.
The result of the binned maximum likelihood fit is shown below. All sources of systematic uncertainties (normalization and shape) are included in this fit.
Event probability discriminant distribution for signal and background processes. All templates are normalized to the best fit value of the maximum likelihood fit result. The inset shows the most sensitive bins of the analysis (EPD>0.7).
Results from full dataset (1078 candidate events):
We use the measured single top cross section to directly measure the CKM matrix element
Single Top Signal Features
We enrich the sample with signal events by
making increasing cuts on our event probability discriminant (EPD) and
look for characteristic changes in these sensitive variables. Although
the uncertainties are large, there is a good agreement between data
and the Monte Carlo simulation including single top.
Interesting Cross Check Analyses
Separate Search for t-channel and s-channel Single Top:
We obtain a s-channel and t-channel single top cross-section very consistent with the Standard Model prediction.
Unconstrained Likelihood Fit:
As a cross-check, we evaluate how sensitive the outcome of the measurement is on the Gaussian constraints of the backgrounds. For this purpose, we perform a five parameter likelihood fit with all Gaussian constraints removed. The result is shown below. The measurement is less precise (uncertainty is increased by about 20%) but the central value remains almost unchanged.
We have updated our search for single top using a Matrix-Element
based analysis and applied it to 1.51 fb-1 of data
taken by the CDF experiment. We include rate and shape systematic
uncertainties in our analysis method. We measure a single top cross-section of
=3.0+1.2-1.1pb. We use a likelihood ratio method
to calculate the signal significance. The observed p-value in 1.51 fb-1
of CDF data is 0.09% (3.1 SD). The expected (median) p-value
in pseudo-experiments is 0.13% (3.0 SD). The cross section measurement is used
to directly determine the CKM matrix element Vtb and we measure
Analysis Changes Since DPF2006