1.
J Synchrotron Radiat
; 29(Pt 6): 1368-1375, 2022 Nov 01.
Article
in English
| MEDLINE
| ID: mdl-36345744
ABSTRACT
Machine learning has recently been applied and deployed at several light source facilities in the domain of accelerator physics. Here, an approach based on machine learning to produce a fast-executing model is introduced 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.