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Point cloud transformers applied to collider physics


Mikuni, Vinicius; Canelli, Florencia (2021). Point cloud transformers applied to collider physics. IOP Conference Series : Materials Science and Engineering, 2(3):035027.

Abstract

Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of transformer networks to learn semantic relationships between sequences in language processing. In this work, we apply a modified transformer network called point cloud transformer as a method to incorporate the advantages of the transformer architecture to an unordered set of particles resulting from collision events. To compare the performance with other strategies, we study jet-tagging applications for highly-boosted particles.

Abstract

Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of transformer networks to learn semantic relationships between sequences in language processing. In this work, we apply a modified transformer network called point cloud transformer as a method to incorporate the advantages of the transformer architecture to an unordered set of particles resulting from collision events. To compare the performance with other strategies, we study jet-tagging applications for highly-boosted particles.

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16 citations in Scopus®
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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 > Artificial Intelligence
Physical Sciences > Human-Computer Interaction
Physical Sciences > Software
Uncontrolled Keywords:Artificial Intelligence, Human-Computer Interaction, Software
Language:English
Date:1 September 2021
Deposited On:23 Dec 2021 05:48
Last Modified:26 Apr 2024 01:38
Publisher:IOP Publishing
ISSN:1757-899X
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1088/2632-2153/ac07f6
  • Content: Published Version
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)