A survey of machine learning-based physics event generation
This is an incremental survey paper for researchers in physics and machine learning, summarizing existing methods without introducing new techniques.
The paper surveys the state-of-the-art in machine learning-based physics event generators for high-energy nuclear and particle physics, reviewing generative models, challenges, and approaches to incorporate physics into designs, while exploring open questions like super-resolution and fidelity.
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.