JEDI-net: a jet identification algorithm based on interaction networks
Eric A. Moreno,Olmo Cerri,Javier Duarte,Javier Duarte,Harvey Newman,Thong Q. Nguyen,Avikar Periwal,Maurizio Pierini,Aidana Serikova,Maria Spiropulu,Jean-Roch Vlimant +10 more
TLDR
In this paper, the performance of a jet identification algorithm based on interaction networks (JEDI-net) was investigated 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.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.read more
Citations
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Graph Neural Networks in Particle Physics
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Graph neural networks in particle physics
TL;DR: In this paper, the authors review various applications of graph neural networks in particle physics, including different graph constructions, model architectures and learning objectives, as well as key open problems in particle particle physics for which graph neural network is promising.
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Interaction networks for the identification of boosted H → b b ¯ decays
Eric A. Moreno,Thong Nguyen,Jean-Roch Vlimant,Olmo Cerri,Harvey B Newman,Avikar Periwal,Maria Spiropulu,Javier Duarte,Maurizio Pierini +8 more
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ABCNet: an attention-based method for particle tagging
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Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
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TL;DR: In this paper, a method for designing optimally heterogeneously quantized versions of deep neural network models for minimum energy, high-accuracy, nanosecond inference and fully automated deployment on chip is introduced.
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