A Survey of Machine Learning-Based Physics Event Generation
A Survey of Machine Learning-Based Physics Event Generation
Yasir Alanazi, Nobuo Sato, Pawel Ambrozewicz, Astrid Hiller-Blin, Wally Melnitchouk, Marco Battaglieri, Tianbo Liu, Yaohang Li
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Survey Track. Pages 4286-4293.
https://doi.org/10.24963/ijcai.2021/588
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.
Keywords:
Machine learning: General
Multidisciplinary topics and applications: General