Toward intelligent control of MeV electrons and protons from kHz repetition rate ultra-intense laser interactions

Authors

• Nathaniel Tamminga, The Ohio State University
• Scott Feister, California State University, Channel Islands
• Kyle D. Frische, Center for Technical Intelligence Studies and Research, Air Force Institute of Technology
• Ronak Desai, The Ohio State University
• Joseph Snyder, Miami University - Hamilton
• John J. Felice, The Ohio State University
• Joseph R. Smith, Marietta College
• Chris Orban, The Ohio State University
• Enam A. Chowdury, The Ohio State University
• Michael L. Dexter, Air Force Institute of TechnologyFollow
• Anil K. Patnaik, Air Force Institute of TechnologyFollow

Document Type

Article

Publication Date

6-2025

Abstract

Ultra-intense laser–matter interactions are often difficult to predict from first principles because of the complexity of plasma processes and the many degrees of freedom relating to the laser and target parameters. An important approach to controlling and optimizing ultra-intense laser interactions involves gathering large datasets and using these data to train statistical and machine learning models. In this paper, we describe experimental efforts to accelerate electrons and protons to ∼MeV energies with this goal in mind. These experiments involve a 1 kHz repetition rate ultra-intense laser system with ∼10 mJ per shot, a peak intensity near 5 × 10¹⁸ W/cm², and a “liquid leaf” target. Improvements to the data acquisition capabilities of this laser system greatly aided this investigation. Generally, we find that the trained models were very effective in controlling the numbers of MeV electrons ejected. The models were less successful at shifting the energy range of ejected electrons. Simultaneous control of the numbers of ∼MeV electrons and the energy range will be the subject of future experimentation using this platform.

Comments

© 2025 Authors. Creative Commons Attribution 4.0 International License

This article is published by AIP, licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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DOI

10.1063/5.0253529

Source Publication

APL Machine Learning (ISSN 2770-9019)

Recommended Citation

Nathaniel Tamminga, Scott Feister, Kyle D. Frische, Ronak Desai, Joseph Snyder, John J. Felice, Joseph R. Smith, Chris Orban, Enam A. Chowdhury, Michael L. Dexter, Anil K. Patnaik; Toward intelligent control of MeV electrons and protons from kHz repetition rate ultra-intense laser interactions. APL Mach. Learn. 1 June 2025; 3 (2): 026115. https://doi.org/10.1063/5.0253529