Towards real-time adaptable machine learning-based photoinjector shaping

Jack Hirschman, Randy Lemons, Ryan Coffee, Federico Belli, Sergio Carbajo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationCLEO: Science and Innovations 2021
PublisherOPTICA Publishing Group
ISBN (Electronic)9781943580910
DOIs
Publication statusPublished - 9 May 2021
EventCLEO: Science and Innovations 2021 - Virtual, Online, United States
Duration: 9 May 202114 May 2021

Conference

ConferenceCLEO: Science and Innovations 2021
Country/TerritoryUnited States
CityVirtual, Online
Period9/05/2114/05/21

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials

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