Compressing PDF sets using generative adversarial networks
- Resource Type
- article
- Authors
- Stefano Carrazza; Juan Cruz-Martinez; Tanjona R. Rabemananjara
- Source
- European Physical Journal C: Particles and Fields, Vol 81, Iss 6, Pp 1-17 (2021)
- Subject
- Astrophysics
QB460-466
Nuclear and particle physics. Atomic energy. Radioactivity
QC770-798
- Language
- English
- ISSN
- 1434-6044
1434-6052
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.