Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin
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 lase...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2023-05-01
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| Series: | Matter and Radiation at Extremes |
| Online Access: | http://dx.doi.org/10.1063/5.0140828 |
| _version_ | 1797771863184637952 |
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| author | Zhi-Wei Lu Xin-Di Hou Feng Wan Yousef I. Salamin Chong Lv Bo Zhang Fei Wang Zhong-Feng Xu Jian-Xing Li |
| author_facet | Zhi-Wei Lu Xin-Di Hou Feng Wan Yousef I. Salamin Chong Lv Bo Zhang Fei Wang Zhong-Feng Xu Jian-Xing Li |
| author_sort | Zhi-Wei Lu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T21:43:40Z |
| format | Article |
| id | doaj.art-cd34585319154392a4c0ca11d65bce74 |
| institution | Directory Open Access Journal |
| issn | 2468-080X |
| language | English |
| last_indexed | 2024-03-12T21:43:40Z |
| publishDate | 2023-05-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | Matter and Radiation at Extremes |
| spelling | doaj.art-cd34585319154392a4c0ca11d65bce742023-07-26T15:10:51ZengAIP Publishing LLCMatter and Radiation at Extremes2468-080X2023-05-0183034401034401-910.1063/5.0140828Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spinZhi-Wei Lu0Xin-Di Hou1Feng Wan2Yousef I. Salamin3Chong Lv4Bo Zhang5Fei Wang6Zhong-Feng Xu7Jian-Xing Li8Ministry 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, ChinaMinistry 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, ChinaMinistry 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, ChinaDepartment of Physics, American University of Sharjah, P.O. Box 26666, Sharjah, United Arab EmiratesDepartment of Nuclear Physics, China Institute of Atomic Energy, P.O. Box 275(7), Beijing 102413, ChinaKey Laboratory of Plasma Physics, Research Center of Laser Fusion, China Academy of Engineering Physics, Mianshan Rd. 64#, Mianyang, Sichuan 621900, ChinaSchool of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, ChinaMinistry 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, ChinaMinistry 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, ChinaThe 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.http://dx.doi.org/10.1063/5.0140828 |
| spellingShingle | Zhi-Wei Lu Xin-Di Hou Feng Wan Yousef I. Salamin Chong Lv Bo Zhang Fei Wang Zhong-Feng Xu Jian-Xing Li Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin Matter and Radiation at Extremes |
| title | Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin |
| title_full | Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin |
| title_fullStr | Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin |
| title_full_unstemmed | Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin |
| title_short | Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin |
| title_sort | diagnosis of ultrafast ultraintense laser pulse characteristics by machine learning assisted electron spin |
| url | http://dx.doi.org/10.1063/5.0140828 |
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