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

Abstract

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.

Authors:
 [1]
  1. Yale Univ., New Haven, CT (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Contributing Org.:
ATLAS Collaboration
OSTI Identifier:
1544185
Grant/Contract Number:  
FG02-92ER40704
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Physics. Conference Series
Additional Journal Information:
Journal Volume: 1085; Journal ID: ISSN 1742-6588
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Paganini, Michela. Machine Learning Algorithms for b-Jet Tagging at the ATLAS Experiment. United States: N. p., 2018. Web. doi:10.1088/1742-6596/1085/4/042031.
Paganini, Michela. Machine Learning Algorithms for b-Jet Tagging at the ATLAS Experiment. United States. doi:10.1088/1742-6596/1085/4/042031.
Paganini, Michela. Sat . "Machine Learning Algorithms for b-Jet Tagging at the ATLAS Experiment". United States. doi:10.1088/1742-6596/1085/4/042031. https://www.osti.gov/servlets/purl/1544185.
@article{osti_1544185,
title = {Machine Learning Algorithms for b-Jet Tagging at the ATLAS Experiment},
author = {Paganini, Michela},
abstractNote = {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.},
doi = {10.1088/1742-6596/1085/4/042031},
journal = {Journal of Physics. Conference Series},
number = ,
volume = 1085,
place = {United States},
year = {2018},
month = {9}
}

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Works referenced in this record:

Long Short-Term Memory
journal, November 1997


Preparing ATLAS reconstruction software for LHC's Run 2
journal, December 2015


LHC Machine
journal, August 2008


The ATLAS Inner Detector commissioning and calibration
journal, August 2010