Anomaly detection with Convolutional Graph Neural Networks

Atkinson, O., Bhardwaj, A., Englert, C. , Ngairangbam, V. S. and Spannowsky, M. (2021) Anomaly detection with Convolutional Graph Neural Networks. Journal of High Energy Physics, 2021, 80. (doi: 10.1007/JHEP08(2021)080)

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Abstract

We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of simultaneously reconstructing edge features and node features. Focusing on latent space based discriminators, we find that such setups provide a promising avenue to isolate new physics and competing SM signatures from sensitivity-limiting QCD jet contributions. We demonstrate the flexibility and broad applicability of this approach using examples of W bosons, top quarks, and exotic hadronically-decaying exotic scalar bosons.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Englert, Professor Christoph and Atkinson, Mr Oliver and Bhardwaj, Ms Akanksha
Authors: Atkinson, O., Bhardwaj, A., Englert, C., Ngairangbam, V. S., and Spannowsky, M.
College/School:College of Science and Engineering > School of Physics and Astronomy
Journal Name:Journal of High Energy Physics
Publisher:Springer
ISSN:1126-6708
ISSN (Online):1029-8479
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Journal of High Energy Physics 2021:80
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
313214STFC Glasgow Physics 2020 DTPDavid IrelandScience and Technology Facilities Council (STFC)ST/V506692/1P&S - Physics & Astronomy
306883Research in Particle Physics Theory - Phenomenology from lattice QCD and collider physicsChristine DaviesScience and Technology Facilities Council (STFC)ST/T000945/1P&S - Physics & Astronomy