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Ultra-intense laser and plasma interactions with their ability to accelerate particles reaching relativistic speed are exciting from a fundamental high-field physics perspective. Such relativistic laser-plasma interaction (RLPI) offers a plethora of critical applications for energy, space, and defense enterprise. At AFIT’s Extreme Light Laboratory (ELL), we have demonstrated such RLPI employing a table-top ∼10mJ, 40 fs laser pulses at a kHz repetition rate that produce different types of secondary radiations via target normal sheath acceleration (TNSA). With our recent demonstration of laser-driven fusion, the secondary radiations generated are neutrons, x-ray emission, and MeV energy electrons and protons—all at a kHz rate. To achieve the high repetition rate, we developed the enabling kHz-repetition-rate-compatible liquid targets in the form of microjets, droplets, and submicron-thick sheets. These targets, combined with high repetition rate diagnostics, enable a unique, real-time feedback loop between the experimental inputs (laser and target parameters) and generated sources (x-rays, electrons, ions, etc.) to develop machine learning (ML)-based control of mixed radiation. The goal of this paper is to provide an overview of the capabilities of ELL, describe the diagnostics and characteristics of the secondary radiation, data analysis, and quasi-real-time ML functionality of this platform that have been developed over the last decade and a half.
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Toward intelligent control of MeV electrons and protons from kHz repetition rate ultra-intense laser interactions
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.
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- Award ID(s):
- 2109222
- PAR ID:
- 10602834
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- APL Machine Learning
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2770-9019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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