A neural network model of a quasiperiodic elliptically polarizing undulator in universal mode. (20th October 2022) - Sheppard, Ryan; Baribeau, Cameron; Pedersen, Tor; Boland, Mark; Bertwistle, Drew
Abstract : 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. 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.