论文标题
在通用模式下的准周期性椭圆形偏振异常的神经网络模型
A Neural Network Model of a Quasi-Periodic Elliptically Polarizing Undulator in Universal Mode
论文作者
论文摘要
机器学习最近已被应用和部署在加速器物理领域的几个光源设施中。我们介绍了一种基于机器学习的方法，以产生快速执行模型，该模型可以预测插入装置上产生的辐射光的极化和能量。本文演示了如何在模拟数据上培训机器学习模型，然后校准为较小，有限的测量数据集，这是一种称为传输学习的技术。该结果将使用户能够有效地确定实现任意光束特性的插入设备设置。
Machine learning has recently been applied and deployed at several light source facilities in the domain of Accelerator Physics. We introduce an approach based on machine learning to produce a fast-executing model that predicts the polarization and energy of the radiated light produced at an insertion device. This paper demonstrates how a machine learning model can be trained on simulated data and later calibrated to a smaller, limited measured data set, a technique referred to as transfer learning. This result will enable users to efficiently determine the insertion device settings for achieving arbitrary beam characteristics.
