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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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
Glasgow Author(s) Enlighten ID:Englert, Professor Christoph and Atkinson, Mr Oliver and Bhardwaj, Dr 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
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