Towards Intelligent Control of MeV Electrons and Protons from kHz Rep-Rate Ultra-Intense Laser-Plasma Interactions
ORAL
Abstract
Although Target normal sheath acceleration (TNSA) is one of the most studied laser-driven ion acceleration methods, predicting the result of laser-plasma interactions in this regime from first principles is difficult due to the complexity of the plasma processes and many degrees of freedom inherent to the laser and target parameters. For the translation from proof of concept experiments to science and engineering applications, precision control of the secondary radiation and particle acceleration is essential.
We present results from an experiment at the Extreme Light Lab (ELL) at the Air Force Institute of Technology. In this experiment, a tabletop 10mJ, 40fs laser pulse at a 1kHz repetition rate is used to produce different types of secondary radiation. The ELL has been upgraded to utilize the Experimental Physics Industrial Control System (EPICS) which, when coupled to high throughput diagnostics, allows for large datasets to be collected. These large datasets can be used to train regression algorithms that aim to optimize the energy and number of electrons produced in the interactions. We present results from the initial closed loop experiment where we look at the data collection process and evaluate the effectiveness of different regression algorithms. We also present plans to extend this to the optimization of protons and other types of secondary radiation.
We present results from an experiment at the Extreme Light Lab (ELL) at the Air Force Institute of Technology. In this experiment, a tabletop 10mJ, 40fs laser pulse at a 1kHz repetition rate is used to produce different types of secondary radiation. The ELL has been upgraded to utilize the Experimental Physics Industrial Control System (EPICS) which, when coupled to high throughput diagnostics, allows for large datasets to be collected. These large datasets can be used to train regression algorithms that aim to optimize the energy and number of electrons produced in the interactions. We present results from the initial closed loop experiment where we look at the data collection process and evaluate the effectiveness of different regression algorithms. We also present plans to extend this to the optimization of protons and other types of secondary radiation.
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Publication: N. Tamminga, S. Feister, K. Frische, R. Desai, J. Snyder, J. Felice, J. Smith, C. Orban, E.<br>Chowdhury, M. Dexter, A. Patnaik, APL Machine Learning 3, e2 (2025). https://doi.org/10.1063/5.0253529
Presenters
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Nathaniel Tamminga
The Ohio State University
Authors
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Nathaniel Tamminga
The Ohio State University
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Scott Feister
California State University, Channel Islands
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Kyle Frische
Air Force Institute of Technology
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Ronak Desai
Ohio State University
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Joseph C Snyder
Miami University
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Joseph R Smith
Marietta College
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Chris Orban
Ohio State University
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Enam A Chowdhury
Ohio State University
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Michael L Dexter
Air Force Institute of Technology
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Anil K Patnaik
U.S. Air Force Institute of Technology (AFIT)