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

TL;DR

This paper introduces a neural network-based $b$ jet identification algorithm at the CDF experiment, which effectively distinguishes $b$ jets from other jets using track information, including single-track jets.

Contribution

The paper presents a novel neural network $b$ tagging algorithm emphasizing track-level information and capable of identifying jets with only one track, improving jet identification.

Findings

Effective discrimination between $b$ and light-flavor jets demonstrated.

Capability to evaluate jets with a single track.

Validation using $Z+1$ jet and $tar{t}$ samples.

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.