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 tt¯ candidate samples, rich in light-flavor and b jets, respectively.