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

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.

References in corpus (8)

• Electron-Ion Collider in China
• Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems
• Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
• Stable and scalable multistage terahertz-driven particle accelerator
• Longitudinal phase-space manipulation with beam-driven plasma wakefields
• Generation and Characterization of Electron Bunches with Ramped Current Profiles in a Dual-Frequency Superconducting Linear Accelerator
• Demonstration of cascaded modulator-chicane micro-bunching of a relativistic electron beam
• Longitudinal phase space synthesis with tailored 3D-printable dielectric-lined waveguides