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arXiv · 1108.4738

An artificial neural network based $b$ jet identification algorithm at the CDF Experiment

J. Freeman·W. Ketchum·J. D. Lewis·S. Poprocki·A. Pronko·V. Rusu·P. Wittich
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Abstract

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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J. Freeman, W. Ketchum, J. D. Lewis, S. Poprocki, A. Pronko, V. Rusu, P. Wittich. 2011-10-21. An artificial neural network based $b$ jet identification algorithm at the CDF Experiment. https://doi.org/10.1016/j.nima.2011.10.024

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