ML Anthology

A Survey of Machine Learning-Based Physics Event Generation

Yasir Alanazi, Nobuo Sato, Pawel Ambrozewicz, Astrid N. Hiller Blin, Wally Melnitchouk, Marco Battaglieri, Tianbo Liu, Yaohang Li
IJCAI 2021 pp. 4286-4293
doi:10.24963/IJCAI.2021/588 /ijcai/2021/alanazi2021ijcai-survey/

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.

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Cite

Text

Alanazi et al. "A Survey of Machine Learning-Based Physics Event Generation." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/588

Markdown

[Alanazi et al. "A Survey of Machine Learning-Based Physics Event Generation." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/alanazi2021ijcai-survey/) doi:10.24963/IJCAI.2021/588

BibTeX

@inproceedings{alanazi2021ijcai-survey,
  title     = {{A Survey of Machine Learning-Based Physics Event Generation}},
  author    = {Alanazi, Yasir and Sato, Nobuo and Ambrozewicz, Pawel and Blin, Astrid N. Hiller and Melnitchouk, Wally and Battaglieri, Marco and Liu, Tianbo and Li, Yaohang},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {4286-4293},
  doi       = {10.24963/IJCAI.2021/588},
  url       = {https://mlanthology.org/ijcai/2021/alanazi2021ijcai-survey/}
}