Skip to main content

Research Repository

Advanced Search

IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection

Atkinson, Oliver; Bhardwaj, Akanksha; Englert, Christoph; Konar, Partha; Ngairangbam, Vishal S.; Spannowsky, Michael

IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection Thumbnail


Oliver Atkinson

Akanksha Bhardwaj

Christoph Englert

Partha Konar

Vishal S. Ngairangbam


Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures. In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has theoretically favorable properties, it also exhibits formidable sensitivity to non-QCD structures.


Atkinson, O., Bhardwaj, A., Englert, C., Konar, P., Ngairangbam, V. S., & Spannowsky, M. (2022). IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection. Frontiers in Artificial Intelligence, 5, Article 943135.

Journal Article Type Article
Acceptance Date Jun 23, 2022
Online Publication Date Jul 22, 2022
Publication Date 2022
Deposit Date Sep 5, 2022
Publicly Available Date Sep 5, 2022
Journal Frontiers in Artificial Intelligence
Print ISSN 2624-8212
Electronic ISSN 2624-8212
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 5
Article Number 943135


Published Journal Article (1.2 Mb)

Publisher Licence URL

Copyright Statement
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

You might also like

Downloadable Citations