Abstract
Hardware-based machine learning for photoinjector manipulation is a promising solution for real-time adaptive electron-beam manipulation. We present preliminary studies towards this goal including simulations of the optical system and early machine learning results.
© 2021 The Author(s)
PDF Article | Presentation VideoMore Like This
Jack Hirschman, Randy Lemons, Peter Kroetz, and Sergio Carbajo
SF3I.3 CLEO: Science and Innovations (CLEO:S&I) 2022
Nicole Neveu, Randy Lemons, Joseph Duris, Yuantao Ding, Agostino Marinelli, Christopher Mayes, Charles Durfee, and Sergio Carbajo
FTu2O.6 CLEO: QELS_Fundamental Science (CLEO:FS) 2021
Ihtesham Khan, M Umar Masood, Lorenzo Tunesi, Paolo Bardella, Enrico Ghillino, Andrea Carena, and Vittorio Curri
JTu3A.32 CLEO: Applications and Technology (CLEO:A&T) 2021