LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows

LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows

In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, because of the upcoming high-luminosity upgrade of the L...

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Bibliographic Details
Main Authors: Breno Orzari, Nadezda Chernyavskaya, Raphael Cobe, Javier Duarte, Jefferson Fialho, Dimitrios Gunopulos, Raghav Kansal, Maurizio Pierini, Thiago Tomei, Mary Touranakou
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:Machine Learning: Science and Technology
Subjects:
generative models
particle physics
high energy physics
hyperparameter tuning
Online Access:https://doi.org/10.1088/2632-2153/ad04ea

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