A new computational framework for particle and spin simulations based on the stochastic Galerkin method

TL;DR

This paper introduces a stochastic Galerkin-based computational framework using Polynomial Chaos Expansion for efficient particle and spin simulations in RF Wien filters, offering a promising alternative to Monte Carlo methods.

Contribution

It presents a novel application of the stochastic Galerkin method with Polynomial Chaos Expansion for beam and spin motion simulation, improving computational efficiency.

Findings

Demonstrates the effectiveness of the stochastic Galerkin method in beam and spin simulations.

Shows significant speed-up compared to Monte Carlo methods.

Provides a new tool for searching for the deuteron electric dipole moment.

Abstract

An implementation of the Polynomial Chaos Expansion is introduced here as a fast solver of the equations of beam and spin motion inside an RF Wien filter. The device shall be used to search for the deuteron electric dipole moment in the COSY storage ring. The new approach is based on the stochastic Galerkin method, and it is shown that this constitutes a new and very powerful alternative to the commonly used Monte-Carlo methods.