An artificial neural network based $b$ jet identification algorithm at   the CDF Experiment
  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. 
J. Freeman, W. Ketchum, J.D. Lewis, S. Poprocki, A. Pronko, V. Rusu,   P. Wittich
hep-ex physics.data-an