A data-driven event generator for Hadron Colliders using Wasserstein Generative Adversarial Network

Suyong Choi, Jae Hoon Lim

Research output: Contribution to journalArticlepeer-review

Abstract

Highly reliable Monte-Carlo event generators and detector simulation programs are important for the precision measurement in the high energy physics. Huge amounts of computing resources are required to produce a sufficient number of simulated events. Moreover, simulation parameters have to be fine-tuned to reproduce situations in the high-energy particle interactions which is not trivial in some phase spaces in physics interests. In this paper, we suggest a new method based on the Wasserstein Generative Adversarial Network (WGAN) that can learn the probability distribution of the real data. Our method is capable of event generation at a very short computing time compared to the traditional MC generators. The trained WGAN is able to reproduce the shape of the real data with high fidelity.

Original languageEnglish
Pages (from-to)482-489
Number of pages8
JournalJournal of the Korean Physical Society
Volume78
Issue number6
DOIs
Publication statusPublished - 2021 Mar

Keywords

  • Deep learning
  • Event generation
  • GAN
  • HEP data
  • WGAN

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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