LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows
Breno Orzari,
Nadezda Chernyavskaya,
Raphael Cobe
et al.
Abstract: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 LHC, there will not be enough computational power or time to match the amount of needed simulated data using MC methods. An alternative approach under study is the usage of machine learning generative methods to fulf… Show more
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