A Neural Network Model of a Quasi-Periodic Elliptically Polarizing Undulator in Universal Mode
Sheppard, Ryan, Cameron Kenneth Baribeau, Tor Pedersen, Mark Boland, Drew Bertwistle · arXiv (Cornell University) · 2022
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.