Publications
Publication Information
| Title | A Survey of Machine Learning Based Physics Event Generation |
| Authors | Yasir Alanazi, Nobuo Sato, Pawel Ambrozewicz, Astrid Hiller Blin, Wolodymyr Melnitchouk, Marco Battaglieri, Tianbo Liu, Yaohang Li |
| JLAB number | JLAB-THY-21-3385 |
| LANL number | arXiv:2106.00643 |
| Other number | DOE/OR/23177-5205 |
| Document Type(s) | (Meeting) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | Yes |
| LDRD Numbers: | |
| Funding Source: | Nuclear Physics (NP) |
|
Meeting Invited Talk Paper compiled for International Joint Conference on Artificial Intelligence (IJCAI) 2021 Proceedings IJCAI-21 Edited By (2021) Page(s) 0588 | |
| Publication Abstract: | Event generators in particle physics play an important role in facilitating studies of high-energy particle reactions. In this paper, we survey the state of the art of machine learning (ML) efforts at building physics event generators. We start from reviewing the ML generative models used in ML-based event generators and their specific challenges. We then discuss various approaches of incorporating physics into the ML model designs to overcome these challenges. Finally, we explore several open questions related to super-resolution, fidelity, and extrapolation for physics event generation based on ML technologies. |
| Experiment Numbers: | other |
| Group: | THEORY CENTER |
| Document: | |
| DOI: | https://doi.org/10.24963/ijcai.2021/588 |
| Accepted Manuscript: | 0588.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |