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...
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| Autori principali: | , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
SpringerOpen
2026-02-01
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| Serie: | Journal of High Energy Physics |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/JHEP02(2026)254 |
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