We present the development and validation of a new multivariate
$b$ jet identification algorithm ("$b$ tagger") used at the CDF experiment
at the Fermilab Tevatron. At collider experiments, $b$ taggers allow
one to distinguish particle jets containing $B$ hadrons from other
jets. Employing feed-forward neural network architectures, this tagger is
unique in its emphasis on using information from individual tracks. This
tagger not only contains the usual advantages of a multivariate technique
such as maximal use of information in a jet and tunable purity/efficiency
operating points, but is also capable of evaluating jets with only a
single track. To demonstrate the effectiveness of the tagger, we employ a
novel method wherein we calculate the false tag rate and tag efficiency
as a function of the placement of a lower threshold on a jet's neural
network output value in $Z+1$ jet and $t\bar{t}$ candidate samples,
rich in light flavor and $b$ jets, respectively.
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Updated: Friday, 2012 February 10 09:40:05 CST automatically from input from Stephanie A Schuler