IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection.
Front Artif Intell
; 5: 943135, 2022.
Article
en En
| MEDLINE
| ID: mdl-35937137
ABSTRACT
Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures. In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has theoretically favorable properties, it also exhibits formidable sensitivity to non-QCD structures.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Front Artif Intell
Año:
2022
Tipo del documento:
Article
País de afiliación:
Reino Unido