Towards Real-time Adaptable Machine Learning-based Photoinjector Shaping
- Resource Type
- Conference
- Authors
- Hirschman, Jack; Lemons, Randy; Coffee, Ryan; Belli, Federico; Carbajo, Sergio
- Source
- 2021 Conference on Lasers and Electro-Optics (CLEO) Lasers and Electro-Optics (CLEO), 2021 Conference on. :1-2 May, 2021
- Subject
- Photonics and Electrooptics
Adaptation models
Machine learning
Real-time systems
Electrooptic effects
- Language
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.