Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin

Lu Zhi-Wei; Hou Xin-Di; Wan Feng; Salamin Yousef I.; Lv Chong; Zhang Bo; Wang Fei; Xu Zhong-Feng; Li Jian-Xing

doi:10.1063/5.0140828

Volume 8 Issue 3

May 2023

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Article Navigation > Matter and Radiation at Extremes > 2023 > 8(3): 034401

Lu Zhi-Wei, Hou Xin-Di, Wan Feng, Salamin Yousef I., Lv Chong, Zhang Bo, Wang Fei, Xu Zhong-Feng, Li Jian-Xing. Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin[J]. Matter and Radiation at Extremes, 2023, 8(3): 034401. doi: 10.1063/5.0140828

Citation:

Lu Zhi-Wei, Hou Xin-Di, Wan Feng, Salamin Yousef I., Lv Chong, Zhang Bo, Wang Fei, Xu Zhong-Feng, Li Jian-Xing. Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin[J]. Matter and Radiation at Extremes, 2023, 8(3): 034401. doi: 10.1063/5.0140828

Citation:

Lu Zhi-Wei, Hou Xin-Di, Wan Feng, Salamin Yousef I., Lv Chong, Zhang Bo, Wang Fei, Xu Zhong-Feng, Li Jian-Xing. Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin[J]. Matter and Radiation at Extremes, 2023, 8(3): 034401. doi: 10.1063/5.0140828

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Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin

doi: 10.1063/5.0140828

1.
Ministry of Education Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Shaanxi Province Key Laboratory of Quantum Information and Quantum Optoelectronic Devices, School of Physics, Xi’an Jiaotong University, Xi’an 710049, China
2.
Department of Physics, American University of Sharjah, P.O. Box 26666, Sharjah, United Arab Emirates
3.
Department of Nuclear Physics, China Institute of Atomic Energy, P.O. Box 275(7), Beijing 102413, China
4.
Key Laboratory of Plasma Physics, Research Center of Laser Fusion, China Academy of Engineering Physics, Mianshan Rd. 64#, Mianyang, Sichuan 621900, China
5.
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China

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Corresponding author: a)Author to whom correspondence should be addressed: wanfeng@xjtu.edu.cn
Received Date: 2022-12-31
Accepted Date: 2023-02-26

Available Online: 2023-05-01

Publish Date: 2023-05-01

Abstract

Abstract

The rapid development of ultrafast ultraintense laser technology continues to create opportunities for studying strong-field physics under extreme conditions. However, accurate determination of the spatial and temporal characteristics of a laser pulse is still a great challenge, especially when laser powers higher than hundreds of terawatts are involved. In this paper, by utilizing the radiative spin-flip effect, we find that the spin depolarization of an electron beam can be employed to diagnose characteristics of ultrafast ultraintense lasers with peak intensities around 10²⁰–10²² W/cm². With three shots, our machine-learning-assisted model can predict, simultaneously, the pulse duration, peak intensity, and focal radius of a focused Gaussian ultrafast ultraintense laser (in principle, the profile can be arbitrary) with relative errors of 0.1%–10%. The underlying physics and an alternative diagnosis method (without the assistance of machine learning) are revealed by the asymptotic approximation of the final spin degree of polarization. Our proposed scheme exhibits robustness and detection accuracy with respect to fluctuations in the electron beam parameters. Accurate measurements of ultrafast ultraintense laser parameters will lead to much higher precision in, for example, laser nuclear physics investigations and laboratory astrophysics studies. Robust machine learning techniques may also find applications in more general strong-field physics scenarios.

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