基于机器学习的相对论电子束时间整形一对多问题的直接求解
Machine learning-based direct solver for one-to-many problems on temporal shaping of relativistic electron beams
摘要：
为了控制相对论电子束的时间分布以满足自由电子激光和等离子体尾场加速等先进科学应用的要求，一种广泛使用的技术是操纵色散项，这被证明是一对多的问题。目前流行的时间整形随机优化方法由于其固有的一对多性质，可能面临计算时间长或有时只能提出一个解的问题。在这里，我们提出了一个实时求解器的时间塑造的一对多问题，借助半监督机器学习方法，条件生成对抗网络（CGAN）。我们证明了CGAN求解器可以学习一对多动态，并且能够准确快速地预测不同自定义时间剖面所需的色散项。这种基于机器学习的求解器有望广泛应用于其他科学领域的一对多问题。
英文摘要：
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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