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
Repository 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