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.
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
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