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ABCNet: an attention-based method for particle tagging


Mikuni, V; Canelli, F (2020). ABCNet: an attention-based method for particle tagging. European Physical Journal Plus, 135(6):463.

Abstract

In high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments. To expand on this concept, we propose a graph neural network enhanced by attention mechanisms called ABCNet. To exemplify the advantages and flexibility of treating collider data as a point cloud, two physically motivated problems are investigated: quark–gluon discrimination and pileup reduction. The former is an event-by-event classification, while the latter requires each reconstructed particle to receive a classification score. For both tasks, ABCNet shows an improved performance compared to other algorithms available.

Abstract

In high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments. To expand on this concept, we propose a graph neural network enhanced by attention mechanisms called ABCNet. To exemplify the advantages and flexibility of treating collider data as a point cloud, two physically motivated problems are investigated: quark–gluon discrimination and pileup reduction. The former is an event-by-event classification, while the latter requires each reconstructed particle to receive a classification score. For both tasks, ABCNet shows an improved performance compared to other algorithms available.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Physics Institute
Dewey Decimal Classification:530 Physics
Scopus Subject Areas:Physical Sciences > General Physics and Astronomy
Uncontrolled Keywords:General Physics and Astronomy
Language:English
Date:1 June 2020
Deposited On:12 Aug 2020 14:50
Last Modified:17 Aug 2020 11:02
Publisher:Springer
ISSN:2190-5444
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1140/epjp/s13360-020-00497-3

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