A Survey of Machine Learning-Based Physics Event Generation

Document Type

Conference Paper

Publication Date

2021

DOI

10.24963/ijcai.2021/588

Publication Title

Proceedings of the Thirtieth International Joint Conference for Artificial Intelligence (IJCAI-21)

Pages

4286-4293

Conference Name

Proceedings of the Thirtieth International Joint Conference for Artificial Intelligence (IJCAI-21), 16-26 August 2021, Virtual, Montreal

Abstract

Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions. We survey the state of the art of machine learning (ML) efforts at building physics event generators. We review ML generative models used in ML-based event generators and their specific challenges, and discuss various approaches of incorporating physics into the ML model designs to overcome these challenges. Finally, we explore some open questions related to super-resolution, fidelity, and extrapolation for physics event generation based on ML technology.

Rights

© 2021 International Joint Conferences on Artificial Intelligence Organization. All rights reserved.

Metadata link included in accordance with publisher policy.

Original Publication Citation

Alanazi, Y., Sato, N., Ambrozewicz, P., Hiller-Blin, A., Melnitchouk, W., Battaglieri, M., Liu, T., & Li, Y. (2021). A survey of machine learning-based physics event generation. In Zhi-Hua Zhou (Ed.), Proceedings of the Thirtieth International Joint Conference for Artificial Intelligence (pp. 4286-4293). International Joint Conferences on Artificial Intelligence (IJCAI-21). https://doi.org/10.24963/ijcai.2021/588

Share

COinS