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Anomaly detection with convolutional Graph Neural Networks

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

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Oliver Atkinson

Akanksha Bhardwaj

Christoph Englert

Vishal S. Ngairangbam


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.


Atkinson, O., Bhardwaj, A., Englert, C., Ngairangbam, V. S., & Spannowsky, M. (2021). Anomaly detection with convolutional Graph Neural Networks. Journal of High Energy Physics, 2021(8),

Journal Article Type Article
Acceptance Date Aug 1, 2021
Online Publication Date Aug 17, 2021
Publication Date 2021
Deposit Date Nov 9, 2021
Publicly Available Date Nov 9, 2021
Journal Journal of High Energy Physics
Print ISSN 1126-6708
Electronic ISSN 1029-8479
Publisher Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Peer Reviewed Peer Reviewed
Volume 2021
Issue 8


Published Journal Article (660 Kb)

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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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