| Autor: |
Oliver Atkinson, Akanksha Bhardwaj, Christoph Englert, Vishal S. Ngairangbam, Michael Spannowsky |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Journal of High Energy Physics, Vol 2021, Iss 8, Pp 1-19 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1029-8479 |
| DOI: |
10.1007/JHEP08(2021)080 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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