Compressing PDF sets using generative adversarial networks

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.

16 pages, code at https://github.com/N3PDF/pycompressor, v2: Final version to be published in EPJC. Modified Fig. 9 and fixed typos in Sec. 6

References in corpus (3)

• Generative Adversarial Networks
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• LHAPDF6: parton density access in the LHC precision era

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