A Neural Network Model of a Quasi-Periodic Elliptically Polarizing Undulator in Universal Mode

Authors:
  • RSRyan Sheppard
    Ryan Sheppard
  • CBCameron Baribeau
    Cameron Baribeau
  • TPTor Pedersen
    Tor Pedersen
  • MBMark Boland
    Mark Boland
  • DBDrew Bertwistle
    Drew Bertwistle
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Abstract
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.
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