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, 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 |
| 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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