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

Suyong Choi, Jae Hoon Lim

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)

    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

    Bibliographical note

    Publisher Copyright:
    © 2021, The Korean Physical Society.

    Keywords

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

    ASJC Scopus subject areas

    • General Physics and Astronomy

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