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

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

14 Aug 2019arXiv:1908.05318links table onlyarchive 2025-07-28

Eric A. Moreno, Olmo Cerri, Javier M. Duarte, Harvey B. Newman, Thong Q. Nguyen, Avikar Periwal, Maurizio Pierini, Aidana Serikova, Maria Spiropulu, Jean-Roch Vlimant

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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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jmduarte/JEDInet-code officialmentioned on GitHubpytorch report
TheKivs/LHC_Jet_Tagging mentioned on GitHub report

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accuracy jmduarte/JEDInet-code/python/JetImageClassifier_INTop_FinalTraining.py official repository ran · honoured contract no licence file found · pointer only · ecedd5ee4ceecd09 · report
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