Machine learning-based direct solver for one-to-many problems on temporal shaping of relativistic electron beams

TL;DR

This paper introduces a real-time machine learning solver using CGANs to efficiently address the complex one-to-many temporal shaping problems of relativistic electron beams, surpassing traditional stochastic methods in speed and flexibility.

Contribution

The paper presents a novel semi-supervised CGAN-based approach for solving one-to-many temporal shaping problems in electron beams, enabling rapid and accurate predictions.

Findings

CGAN can learn one-to-many dynamics effectively.

The solver predicts dispersion terms quickly and accurately.

Potential for application in other scientific one-to-many problems.

Abstract

To control the temporal profile of a relativistic electron beam to meet requirements of various advanced scientific applications like free-electron-laser and plasma wakefield acceleration, a widely-used technique is to manipulate the dispersion terms which turns out to be one-to-many problems. Due to their intrinsic one-to-many property, current popular stochastic optimization approaches on temporal shaping may face the problems of long computing time or sometimes suggesting only one solution. Here we propose a real-time solver for one-to-many problems of temporal shaping, with the aid of a semi-supervised machine learning method, the conditional generative adversarial network (CGAN). We demonstrate that the CGAN solver can learn the one-to-many dynamics and is able to accurately and quickly predict the required dispersion terms for different custom temporal profiles. This machine learning-based solver is expected to have the potential for wide applications to one-to-many problems in other scientific fields.