Document Type
Article
Publication Date
2024
DOI
10.1103/PhysRevD.110.016030
Publication Title
Physical Review D
Volume
110
Issue
1
Pages
016030 (1-14)
Abstract
Generative artificial intelligence is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like the Continuous Electron Beam Accelerator Facility and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or eventwide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine-learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model.
Rights
© 2024 The Authors.
Published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP3.
Original Publication Citation
Devlin, P., Qiu, J. W., Ringer, F., & Sato, N. (2024). Diffusion model approach to simulating electron-proton scattering events. Physical Review D, 110(1), 1-14, Article 016030. https://doi.org/10.1103/PhysRevD.110.016030
ORCID
0000-0002-5939-3510 (Ringer)
Repository Citation
Devlin, Peter; Qiu, Jian-Wei; Ringer, Felix; and Sato, Nobuo, "Diffusion Model Approach to Simulating Electron-Proton Scattering Events" (2024). Physics Faculty Publications. 869.
https://digitalcommons.odu.edu/physics_fac_pubs/869