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.

Graphical abstract

Keywords

beam shaping / one-to-many problem / machine learning

References

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Acknowledgements

The authors thank Dr. Juhao Wu for nice discussion and suggestions. This work was supported by National Natural Science Foundation of China (No. 11922512), Youth Innovation Promotion Association of Chinese Academy of Sciences (No. Y201904) and National Key R&D Program of China (No. 2016YFA0401900).

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