A survey of machine learning-based physics event generation
High Energy Physics - Phenomenology
2021-12-30 v1 Machine Learning
Nuclear Experiment
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.
Keywords
Cite
@article{arxiv.2106.00643,
title = {A survey of machine learning-based physics event generation},
author = {Yasir Alanazi and N. Sato and Pawel Ambrozewicz and Astrid N. Hiller Blin and W. Melnitchouk and Marco Battaglieri and Tianbo Liu and Yaohang Li},
journal= {arXiv preprint arXiv:2106.00643},
year = {2021}
}
Comments
8 pages, 2 figures, paper accepted for publication in IJCAI2021