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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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Toward direct spatial and intensity characterization of ultra-high-intensity laser pulses using ponderomotive scattering of free electrons
Spatial distributions of electrons ionized and scattered from ultra-low-pressure gases are proposed and experimentally demonstrated as a method to directly measure the intensity of an ultra-high-intensity laser pulse. Analytic models relating the peak scattered electron energy to the peak laser intensity are derived and compared to paraxial Runge–Kutta simulations highlighting two models suitable for describing electrons scattered from weakly paraxial beams (f#>5) for intensities in the range of 1018−1021 W cm−2. Scattering energies are shown to be dependent on gas species, emphasizing the need for specific gases for given intensity ranges. Direct measurements of the laser intensity at full power of two laser systems are demonstrated, both showing a good agreement between indirect methods of intensity measurement and the proposed method. One experiment exhibited the role of spatial aberrations in the scattered electron distribution, motivating a qualitative study on the effect. We propose the use of convolutional neural networks as a method for extracting quantitative information on the spatial structure of the laser at full power. We believe the presented technique to be a powerful tool that can be immediately implemented in many high-power laser facilities worldwide.
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- Award ID(s):
- 2308905
- PAR ID:
- 10591978
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- Physics of Plasmas
- Volume:
- 30
- Issue:
- 8
- ISSN:
- 1070-664X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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