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Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm

Konar, Partha; Ngairangbam, Vishal S.; Spannowsky, Michael

Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm Thumbnail


Authors

Partha Konar

Vishal S. Ngairangbam



Abstract

Hadronic signals of new-physics origin at the Large Hadron Collider can remain hidden within the copiously produced hadronic jets. Unveiling such signatures require highly performant deep-learning algorithms. We construct a class of Graph Neural Networks (GNN) in the message-passing formalism that makes the network output infra-red and collinear (IRC) safe, an important criterion satisfied within perturbative QCD calculations. Including IRC safety of the network output as a requirement in the construction of the GNN improves its explainability and robustness against theoretical uncertainties in the data. We generalise Energy Flow Networks (EFN), an IRC safe deep-learning algorithm on a point cloud, defining energy weighted local and global readouts on GNNs. Applying the simplest of such networks to identify top quarks, W bosons and quark/gluon jets, we find that it outperforms state-of-the-art EFNs. Additionally, we obtain a general class of graph construction algorithms that give structurally invariant graphs in the IRC limit, a necessary criterion for the IRC safety of the GNN output.

Citation

Konar, P., Ngairangbam, V. S., & Spannowsky, M. (2022). Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm. Journal of High Energy Physics, 2022(2), Article 60. https://doi.org/10.1007/jhep02%282022%29060

Journal Article Type Article
Acceptance Date Jan 25, 2022
Online Publication Date Feb 8, 2022
Publication Date 2022
Deposit Date May 4, 2022
Publicly Available Date May 5, 2022
Journal Journal of High Energy Physics
Print ISSN 1126-6708
Publisher Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Peer Reviewed Peer Reviewed
Volume 2022
Issue 2
Article Number 60
DOI https://doi.org/10.1007/jhep02%282022%29060

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.





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