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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