Machine Learning Algorithms for b-Jet Tagging at the ATLAS Experiment

Journal Article · 01 September 2018 · Journal of Physics. Conference Series

DOI: https://doi.org/10.1088/1742-6596/1085/4/042031 · OSTI ID:1544185

Paganini, Michela ^[1]

---

1. Yale Univ., New Haven, CT (United States)

The separation of b-quark initiated jets from those coming from lighter quark flavors (b-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful b-tagging algorithms combine information from low-level taggers, exploiting reconstructed track and vertex information, into machine learning classifiers. The potential of modern deep learning techniques is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• Contributing Organization: ATLAS Collaboration
• Grant/Contract Number: FG02-92ER40704
• OSTI ID: 1544185
• Journal Information: Journal of Physics. Conference Series, Vol. 1085; ISSN 1742-6588
• Publisher: IOP PublishingCopyright Statement
• Country of Publication: United States
• Language: English