Compressing PDF sets using generative adversarial networks

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.

Automated summary

The algorithm proposed involves using synthetic replicas generated by a generative adversarial network (GAN) for compressing parton densities, leading to a compressed set with fewer replicas and better representation of the original probability distribution. Additionally, the potential of GAN as an alternative mechanism to avoid fitting a large number of replicas is discussed.