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Title: End-to-end jet classification of quarks and gluons with the CMS Open Data

Abstract

We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data. We highlight the importance of precise spatial information and demonstrate competitive performance to existing state-of-the-art jet classifiers. We further generalize the end-to-end approach to event-level classification of quark vs. gluon di-jet QCD events. We compare the fully end-to-end approach to using hand-engineered features and demonstrate that the end-to-end algorithm is robust against the effects of underlying event and pile-up.

Authors:
; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Carnegie Mellon Univ., Pittsburgh, PA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP); European Commission (EC)
OSTI Identifier:
1638100
Alternate Identifier(s):
OSTI ID: 1684625; OSTI ID: 1755516
Grant/Contract Number:  
SC0010118; 765710
Resource Type:
Published Article
Journal Name:
Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
Additional Journal Information:
Journal Name: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment Journal Volume: 977 Journal Issue: C; Journal ID: ISSN 0168-9002
Publisher:
Elsevier
Country of Publication:
Netherlands
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Machine learning; jet images; end-to-end; CMS Open Data; convolutional neural network; LHC; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Andrews, M., Alison, J., An, S., Burkle, B., Gleyzer, S., Narain, M., Paulini, M., Poczos, B., and Usai, E. End-to-end jet classification of quarks and gluons with the CMS Open Data. Netherlands: N. p., 2020. Web. https://doi.org/10.1016/j.nima.2020.164304.
Andrews, M., Alison, J., An, S., Burkle, B., Gleyzer, S., Narain, M., Paulini, M., Poczos, B., & Usai, E. End-to-end jet classification of quarks and gluons with the CMS Open Data. Netherlands. https://doi.org/10.1016/j.nima.2020.164304
Andrews, M., Alison, J., An, S., Burkle, B., Gleyzer, S., Narain, M., Paulini, M., Poczos, B., and Usai, E. Thu . "End-to-end jet classification of quarks and gluons with the CMS Open Data". Netherlands. https://doi.org/10.1016/j.nima.2020.164304.
@article{osti_1638100,
title = {End-to-end jet classification of quarks and gluons with the CMS Open Data},
author = {Andrews, M. and Alison, J. and An, S. and Burkle, B. and Gleyzer, S. and Narain, M. and Paulini, M. and Poczos, B. and Usai, E.},
abstractNote = {We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data. We highlight the importance of precise spatial information and demonstrate competitive performance to existing state-of-the-art jet classifiers. We further generalize the end-to-end approach to event-level classification of quark vs. gluon di-jet QCD events. We compare the fully end-to-end approach to using hand-engineered features and demonstrate that the end-to-end algorithm is robust against the effects of underlying event and pile-up.},
doi = {10.1016/j.nima.2020.164304},
journal = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},
number = C,
volume = 977,
place = {Netherlands},
year = {2020},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.nima.2020.164304

Figures / Tables:

Table 1 Table 1: Number of training events per class passing event selection, by PU run era. Only the leading-$𝑝_𝑇$ jet in the event is used for the jet classification studies.

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