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arXiv · 2103.06594

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

Jinyu Wan·Yi Jiao
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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.

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Jinyu Wan, Yi Jiao. 2021-03-11. Machine learning-based direct solver for one-to-many problems on temporal shaping of relativistic electron beams. https://doi.org/10.1007/s11467-022-1205-y

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