Atkinson, O., Bhardwaj, A., Englert, C. , Konar, P., Ngairangbam, V. S. and Spannowsky, M. (2022) IRC-safe Graph Autoencoder for unsupervised anomaly detection. Frontiers in Artificial Intelligence, 5, 943135. (doi: 10.3389/frai.2022.943135) (PMID:35937137) (PMCID:PMC9352857)
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Abstract
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.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Atkinson, Mr Oliver and Bhardwaj, Dr Akanksha and Englert, Professor Christoph |
Authors: | Atkinson, O., Bhardwaj, A., Englert, C., Konar, P., Ngairangbam, V. S., and Spannowsky, M. |
College/School: | College of Science and Engineering > School of Physics and Astronomy |
Journal Name: | Frontiers in Artificial Intelligence |
Publisher: | Frontiers Media |
ISSN: | 2624-8212 |
ISSN (Online): | 2624-8212 |
Copyright Holders: | Copyright © 2022 Atkinson, Bhardwaj, Englert, Konar, Ngairangbam and Spannowsky |
First Published: | First published in Frontiers in Artificial Intelligence 5: 943135 |
Publisher Policy: | Reproduced under a Creative Commons License |
Data DOI: | 10.5281/zenodo.2603256 |
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