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

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 (2020) JEDI-net: a jet identification algorithm based on interaction networks. European Physical Journal C, 80 (1). Art. No. 58. ISSN 1434-6044. https://resolver.caltech.edu/CaltechAUTHORS:20190903-141154280

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1140/epjc/s10052-020-7608-4DOIArticle
https://arxiv.org/abs/1908.05318arXivDiscussion Paper
ORCID:
AuthorORCID
Cerri, Olmo0000-0002-2191-0666
Newman, Harvey B.0000-0003-0964-1480
Nguyen, Thong Q.0000-0003-3954-5131
Pierini, Maurizio0000-0003-1939-4268
Spiropulu, Maria0000-0001-8172-7081
Additional Information:© 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Funded by SCOAP3. Received: 10 September 2019; Accepted: 3 January 2020; First Online: 25 January 2020. We are grateful to Caltech and the Kavli Foundation for their support of undergraduate student research in cross-cutting areas of machine learning and domain sciences. We would also like to thank the Taylor W. Lawrence Research Fellowship and Mellon Mays Fellowship for supporting E. A. M. and making this research effort possible. This work was conducted at “iBanks,” the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of “iBanks”. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no 772369) and is partially supported by the U.S. Department of Energy, Office of High Energy Physics Research under Caltech Contract No. DE-SC0011925. J. M. D. is supported by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.
Group:CMS@Caltech
Funders:
Funding AgencyGrant Number
Taylor W. Lawrence Research FellowshipUNSPECIFIED
Mellon Mays FellowshipUNSPECIFIED
European Research Council (ERC)772369
Department of Energy (DOE)DE-SC0011925
Department of Energy (DOE)DE-AC02-07CH11359
SCOAP3UNSPECIFIED
Issue or Number:1
Record Number:CaltechAUTHORS:20190903-141154280
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190903-141154280
Official Citation:Moreno, E.A., Cerri, O., Duarte, J.M. et al. Eur. Phys. J. C (2020) 80: 58. https://doi.org/10.1140/epjc/s10052-020-7608-4
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98399
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 Sep 2019 22:54
Last Modified:09 Mar 2020 13:19

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