Speaker
Description
Our work addresses the challenge of estimating spin polarization in high-energy electron and positron storage rings, such as the ESR of the Electron-Ion Collider (EIC) at BNL and those in the electron/positron Future Circular Collider (FCC-ee) at CERN as well as those in the proposed Circular Electron Positron Collider (CEPC).
We model the spin and orbital motion of particle bunches using the recently developed spin-orbit Fokker-Planck equation, a kinetic-type partial differential equation. In this talk/poster, we propose a novel machine learning approach leveraging a randomized Fourier neural network framework*, specifically designed to solve linear space-time kinetic PDEs. We will present a detailed analysis of the spin-orbit Fokker-Planck model, highlight its relevance to spin polarization studies, and share preliminary results demonstrating the network's performance on simplified kinetic PDEs. Additionally, we will discuss the numerical analysis and validity of the proposed method in the context of beam dynamical applications.
Footnotes
- K. Heinemann, D. Appelo, D. P. Barber, O. Beznosov, and J. A. Ellison. Int. Journal of Mod. Phys. A, Vol. 34, 1942032 (2019). See also: arXiv:2101.08955 [physics.acc-ph]
** O. Davis, G. Geraci, and M. Motamed. To appear in SIAM J. Sci. Comp. (2025). See also: arXiv:2407.11894 [cs.LG].
Funding Agency
Supported by U.S. Department of Energy, Office of Science, under Award Numbers
DE-SC0018008 and DE-SC0025476
| Region represented | America |
|---|---|
| Paper preparation format | LaTeX |
