Graph theory inspired anomaly detection at the LHC
Abstract Designing model-independent anomaly detection algorithms for analyzing LHC data remains a central challenge in the search for new physics, due to the high dimensionality of collider events. In this work, we develop a graph autoencoder as an unsupervised, model-agnostic tool for anomaly dete...
Tallennettuna:
| Päätekijät: | , , , |
|---|---|
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
SpringerOpen
2026-02-01
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| Sarja: | Journal of High Energy Physics |
| Aiheet: | |
| Linkit: | https://doi.org/10.1007/JHEP02(2026)254 |
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