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Anomaly detection with Convolutional Graph Neural Networks
- Source :
- JHEP 08 (2021) 080
- Publication Year :
- 2021
-
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.<br />Comment: Added two appendices, minor modifications in text, no changes in result, matches accepted version in JHEP
- Subjects :
- High Energy Physics - Phenomenology
High Energy Physics - Experiment
Subjects
Details
- Database :
- arXiv
- Journal :
- JHEP 08 (2021) 080
- Publication Type :
- Report
- Accession number :
- edsarx.2105.07988
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1007/JHEP08(2021)080