Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

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We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how different design choices (e.g., event represe...

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Detalles Bibliográficos
Autores principales: Jawahar, Pratik, Aarrestad, Thea, Chernyavskaya, Nadezda, Pierini, Maurizio, Wozniak, Kinga A., Ngadiuba, Jennifer, Duarte, Javier, Tsan, Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919050/
https://www.ncbi.nlm.nih.gov/pubmed/35295683
http://dx.doi.org/10.3389/fdata.2022.803685

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919050/
https://www.ncbi.nlm.nih.gov/pubmed/35295683
http://dx.doi.org/10.3389/fdata.2022.803685

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