Measurement of Single Top Quark Production in L=3.2 fb-1
Florencia Canelli1, Bruno Casal Larana2, Peter Dong4, Bernd Stelzer5, Rainer Wallny3
1University of Chicago
2Universidad de Cantabria
3University of California, Los Angeles
4Illinois Mathematics and Science Academy
5Institute for Particle Physics Canada, Simon Fraser University
We present a search for electroweak single top quark production using
of CDF II data collected at the Tevatron in proton-antiproton collisions at a
center-of-mass energy of 1.96 TeV. The analysis employs a
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
2.5+0.7-0.6pb, assuming a top quark mass of 175
The probability that the observed excess originated
from a background fluctuation (p-value) is 1×10-6 (4.3σ) and the expected (median)
p-value in pseudo-experiments is 4.7×10-7 (4.9σ).
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
or three 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, tt-bar, Wcc-bar, Wc+jet
We evaluate the event probability discriminant in the W+2jets sample and W+3jets sample. The corresponding templates (normalized to unit area) are shown below for W+2jet events (left) and W+3jets events (right).
To quantify the single top content in the data, we perform a binned maximum likelihood fit to the data. 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 (δi). The likelihood function is reduced through a standard Bayesian marginalization technique.
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
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
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 lepton + 4 jets sample (2.7 fb-1), control sample which is mainly 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.
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. For shape comparisons, the predicted Monte Carlos
shapes are normalized to the data.
We have updated our search for single top using a Matrix-Element
based analysis and applied it to 3.2 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
We use a likelihood ratio method
to calculate the signal significance. The observed p-value in 3.2 fb-1
of CDF data is 1×10-6 (4.3σ) and the expected (median)
p-value in pseudo-experiments is 4.7×10-7 (4.9σ).