Ryan Sheppard
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Ryan Sheppard has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Cameron Baribeau
Canadian Light Source Inc., 44 Innovation Blvd., Saskatoon, SK S7N 2V3, Canada
1 shared publication - Drew Bertwistle
Canadian Light Source, 44 Innovation Blvd, Saskatoon, Saskatchewan, Canada
1 shared publication - Mark James Boland
University of Saskatchewan and the Canadian Light Source
1 shared publication - Tor M. Pedersen
Canadian Light Source
1 shared publication
Latest Publications
- Open Access
- 20 October 2022
- Published by International Union of Crystallography (IUCr) in Journal of Synchrotron Radiation
- Vol. 29 (6) , 1368-1375
A neural network model of a quasiperiodic elliptically polarizing undulator in universal mode
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.
- 20 October 2022
- Published by International Union of Crystallography (IUCr) in Journal of Synchrotron Radiation
- Vol. 29 (6) , 1368-1375
Open AccessA neural network model of a quasiperiodic elliptically polarizing undulator in universal mode
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.