We present a compression algorithm for parton densities using synthetic replicas generated from the training of a generative adversarial network (GAN). The generated replicas are used to further enhance the statistics of a given Monte Carlo PDF set prior to compression. This results in a compression methodology that is able to provide a compressed set with smaller number of replicas and a more adequate representation of the original probability distribution. We also address the question of whether the GAN could be used as an alternative mechanism to avoid the fitting of large number of replicas.

Compressing PDF sets using generative adversarial networks / S. Carrazza, C. Juan, T.R. Rabemananjara. - In: EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS. - ISSN 1434-6052. - 81(2021 Jun 21).

Compressing PDF sets using generative adversarial networks

S. Carrazza
Primo
;
C. Juan
Secondo
;
T.R. Rabemananjara
Ultimo
2021

Abstract

We present a compression algorithm for parton densities using synthetic replicas generated from the training of a generative adversarial network (GAN). The generated replicas are used to further enhance the statistics of a given Monte Carlo PDF set prior to compression. This results in a compression methodology that is able to provide a compressed set with smaller number of replicas and a more adequate representation of the original probability distribution. We also address the question of whether the GAN could be used as an alternative mechanism to avoid the fitting of large number of replicas.
High Energy Physics; Phenomenology; High Energy Physics; Phenomenology; High Energy Physics; Experiment;
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
9-apr-2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/852619
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