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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
Lenguaje:	eng
Publicado:	2021
Materias:	physics.data-an Other Fields of Physics hep-ex Particle Physics - Experiment hep-ph Particle Physics - Phenomenology
Acceso en línea:	https://dx.doi.org/10.3389/fdata.2022.803685 http://cds.cern.ch/record/2784906

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