Data driven methods to recognize patterns in EIC weak-strong simulation
Year: 2025
Type: conference-paper
Abstract: Beam-Beam simulations are currently being studied in preparation for future EIC experiments to study beam-beam effects and, in turn, maximize luminosity. Weak-strong methods are studied for single-particle dynamics during collision. 1 million macro-particles for 1 million turns are typically tracked, corresponding to only 10 seconds in the EIC. The goal of this study is to predict beam properties over the scale of hours. A potential solution focuses on using data-driven methods such as machine learning methods to analyze and extend the insights of the beam properties such as long-term nonlinear effects. This would aid in long-term predictions where results would be more efficiently acquired than a typical tracking simulation. Some limitations such as inaccurate predictions and spatial complexity are also discussed. These methods can then be applied to strong-strong simulations in the future studies.
Source: JACOW
Authors: Fung,William, Xu,Derong, Hao,Yue
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Funder: U.S. Department of Energy
OpenAlex ID: W7104265135
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