Towards real-time adaptable machine learning-based photoinjector shaping

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

doi:10.1364/CLEO_SI.2021.STh1B.7

Towards real-time adaptable machine learning-based photoinjector shaping

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	CLEO: Science and Innovations 2021
Publisher	OPTICA Publishing Group
ISBN (Electronic)	9781943580910
DOIs	https://doi.org/10.1364/CLEO_SI.2021.STh1B.7
Publication status	Published - 9 May 2021
Event	CLEO: Science and Innovations 2021 - Virtual, Online, United States Duration: 9 May 2021 → 14 May 2021

Conference

Conference	CLEO: Science and Innovations 2021
Country/Territory	United States
City	Virtual, Online
Period	9/05/21 → 14/05/21

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

Access to Document

10.1364/CLEO_SI.2021.STh1B.7

Cite this

@inproceedings{ab647da8e05d4b84abe54de568286f46,

title = "Towards real-time adaptable machine learning-based photoinjector shaping",

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.",

author = "Jack Hirschman and Randy Lemons and Ryan Coffee and Federico Belli and Sergio Carbajo",

note = "Publisher Copyright: {\textcopyright} OSA 2021, {\textcopyright} 2021 The Author(s); CLEO: Science and Innovations 2021 ; Conference date: 09-05-2021 Through 14-05-2021",

year = "2021",

month = may,

day = "9",

doi = "10.1364/CLEO_SI.2021.STh1B.7",

language = "English",

booktitle = "CLEO: Science and Innovations 2021",

publisher = "OPTICA Publishing Group",

}

TY - GEN

T1 - Towards real-time adaptable machine learning-based photoinjector shaping

AU - Hirschman, Jack

AU - Lemons, Randy

AU - Coffee, Ryan

AU - Belli, Federico

AU - Carbajo, Sergio

PY - 2021/5/9

Y1 - 2021/5/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85120062389&partnerID=8YFLogxK

U2 - 10.1364/CLEO_SI.2021.STh1B.7

DO - 10.1364/CLEO_SI.2021.STh1B.7

M3 - Conference contribution

AN - SCOPUS:85120062389

BT - CLEO: Science and Innovations 2021

PB - OPTICA Publishing Group

T2 - CLEO: Science and Innovations 2021

Y2 - 9 May 2021 through 14 May 2021

ER -

Towards real-time adaptable machine learning-based photoinjector shaping

Abstract

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this