期刊
EUROPEAN PHYSICAL JOURNAL C
卷 80, 期 1, 页码 -出版社
SPRINGER
DOI: 10.1140/epjc/s10052-020-7608-4
关键词
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资金
- Kavli Foundation
- Caltech
- Taylor W. Lawrence Research Fellowship
- Mellon Mays Fellowship
- NVIDIA
- SuperMicro
- European Research Council (ERC) under the European Union [772369]
- U.S. Department of Energy, Office of High Energy Physics Research under Caltech [DE-SC0011925]
- Fermi Research Alliance, LLC [DE-AC02-07CH11359]
- U.S. Department of Energy, Office of Science, Office of High Energy Physics
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.
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