Speaker
Description
Our work addresses the challenge of estimating spin po-
larization in high-energy electron and positron storage rings,
such as the Electron Storage Ring (ESR) of the Electron-Ion
Collider (EIC) at Brookhaven National Lab (BNL) and those
in the electron/positron Future Circular Collider (FCC-ee)
at CERN. We model the spin and orbital motion of particle
bunches using the recently introduced spin-orbit Fokker-
Planck (SOFP) equation, a linear time-evolution partial dif-
ferential equation (PDE). In this paper, we propose a novel
machine learning (ML) approach leveraging a randomized
Fourier neural network (rFNN) framework*, specifically de-
signed to solve linear PDEs. We will discuss the SOFP high-
light its relevance to spin polarization studies, and share pre-
liminary results demonstrating the network’s performance
on the Poisson problem.
Funding Agency
Supported by U.S. Department of Energy, Office of Science, under Award Numbers
DE-SC0018008 and DE-SC0025476
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].
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