Towards Intelligent Control of MeV Electrons and Protons from kHz Repetition Rate Ultra-Intense Laser Interactions
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910997557542912 |
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| author | Tamminga, Nathaniel Feister, Scott Frische, Kyle D. Desai, Ronak Snyder, Joseph Felice, John J. Smith, Joseph R. Orban, Chris Chowdhury, Enam A. Dexter, Michael L. Patnaik, Anil K. |
| author_facet | Tamminga, Nathaniel Feister, Scott Frische, Kyle D. Desai, Ronak Snyder, Joseph Felice, John J. Smith, Joseph R. Orban, Chris Chowdhury, Enam A. Dexter, Michael L. Patnaik, Anil K. |
| contents | 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 data sets and using this data to train statistical and machine learning models. In this paper we describe experimental efforts to accelerate electrons and protons to $\sim$MeV energies with this goal in mind. These experiments involve a 1 kHz repetition rate ultra-intense laser system with $\sim$10mJ per shot, a peak intensity near $5 \times 10^{18}$ W/cm$^{2}$, 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 for 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 $\sim$MeV electrons and the energy range will be the subject of future experimentation using this platform. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_21726 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Intelligent Control of MeV Electrons and Protons from kHz Repetition Rate Ultra-Intense Laser Interactions Tamminga, Nathaniel Feister, Scott Frische, Kyle D. Desai, Ronak Snyder, Joseph Felice, John J. Smith, Joseph R. Orban, Chris Chowdhury, Enam A. Dexter, Michael L. Patnaik, Anil K. Plasma Physics 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 data sets and using this data to train statistical and machine learning models. In this paper we describe experimental efforts to accelerate electrons and protons to $\sim$MeV energies with this goal in mind. These experiments involve a 1 kHz repetition rate ultra-intense laser system with $\sim$10mJ per shot, a peak intensity near $5 \times 10^{18}$ W/cm$^{2}$, 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 for 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 $\sim$MeV electrons and the energy range will be the subject of future experimentation using this platform. |
| title | Towards Intelligent Control of MeV Electrons and Protons from kHz Repetition Rate Ultra-Intense Laser Interactions |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2503.21726 |