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Title: JEDI-net: a jet identification algorithm based on interaction networks

Abstract

We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The jet dynamics are described as a set of one-to-one interactions between the jet constituents. Based on a representation learned from these interactions, the jet is associated to one of the considered categories. Unlike other architectures, the JEDI-net models achieve their performance without special handling of the sparse input jet representation, extensive pre-processing, particle ordering, or specific assumptions regarding the underlying detector geometry. The presented models give better results with less model parameters, offering interesting prospects for LHC applications.

Authors:
 [1];  [1]; ORCiD logo [2];  [1];  [1];  [1];  [3];  [1];  [1];  [1]
  1. California Inst. of Technology (CalTech), Pasadena, CA (United States)
  2. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Univ. of California, San Diego, CA (United States)
  3. European Organization for Nuclear Research (CERN), Geneva (Switzerland)
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1561543
Report Number(s):
arXiv:1908.05318; FERMILAB-PUB-19-360-PPD
Journal ID: ISSN 1434-6044; oai:inspirehep.net:1749751
Grant/Contract Number:  
AC02-07CH11359; SC0011925
Resource Type:
Accepted Manuscript
Journal Name:
European Physical Journal. C, Particles and Fields
Additional Journal Information:
Journal Volume: 80; Journal Issue: 1; Journal ID: ISSN 1434-6044
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Moreno, Eric A., Cerri, Olmo, Duarte, Javier M., Newman, Harvey B., Nguyen, Thong Q., Periwal, Avikar, Pierini, Maurizio, Serikova, Aidana, Spiropulu, Maria, and Vlimant, Jean-Roch. JEDI-net: a jet identification algorithm based on interaction networks. United States: N. p., 2020. Web. doi:10.1140/epjc/s10052-020-7608-4.
Moreno, Eric A., Cerri, Olmo, Duarte, Javier M., Newman, Harvey B., Nguyen, Thong Q., Periwal, Avikar, Pierini, Maurizio, Serikova, Aidana, Spiropulu, Maria, & Vlimant, Jean-Roch. JEDI-net: a jet identification algorithm based on interaction networks. United States. doi:10.1140/epjc/s10052-020-7608-4.
Moreno, Eric A., Cerri, Olmo, Duarte, Javier M., Newman, Harvey B., Nguyen, Thong Q., Periwal, Avikar, Pierini, Maurizio, Serikova, Aidana, Spiropulu, Maria, and Vlimant, Jean-Roch. Sat . "JEDI-net: a jet identification algorithm based on interaction networks". United States. doi:10.1140/epjc/s10052-020-7608-4. https://www.osti.gov/servlets/purl/1561543.
@article{osti_1561543,
title = {JEDI-net: a jet identification algorithm based on interaction networks},
author = {Moreno, Eric A. and Cerri, Olmo and Duarte, Javier M. and Newman, Harvey B. and Nguyen, Thong Q. and Periwal, Avikar and Pierini, Maurizio and Serikova, Aidana and Spiropulu, Maria and Vlimant, Jean-Roch},
abstractNote = {We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The jet dynamics are described as a set of one-to-one interactions between the jet constituents. Based on a representation learned from these interactions, the jet is associated to one of the considered categories. Unlike other architectures, the JEDI-net models achieve their performance without special handling of the sparse input jet representation, extensive pre-processing, particle ordering, or specific assumptions regarding the underlying detector geometry. The presented models give better results with less model parameters, offering interesting prospects for LHC applications.},
doi = {10.1140/epjc/s10052-020-7608-4},
journal = {European Physical Journal. C, Particles and Fields},
number = 1,
volume = 80,
place = {United States},
year = {2020},
month = {1}
}

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