Zhijie Lu, X. Hou, F. Wan, Y. Salamin, C. Lv, Bo Zhang, Feijie Wang, Zhongxun Xu, Jian-Xing Li
{"title":"Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin","authors":"Zhijie Lu, X. Hou, F. Wan, Y. Salamin, C. Lv, Bo Zhang, Feijie Wang, Zhongxun Xu, Jian-Xing Li","doi":"10.1063/5.0140828","DOIUrl":null,"url":null,"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.","PeriodicalId":54221,"journal":{"name":"Matter and Radiation at Extremes","volume":"19 1","pages":""},"PeriodicalIF":4.8000,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Matter and Radiation at Extremes","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1063/5.0140828","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1
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.
期刊介绍:
Matter and Radiation at Extremes (MRE), is committed to the publication of original and impactful research and review papers that address extreme states of matter and radiation, and the associated science and technology that are employed to produce and diagnose these conditions in the laboratory. Drivers, targets and diagnostics are included along with related numerical simulation and computational methods. It aims to provide a peer-reviewed platform for the international physics community and promote worldwide dissemination of the latest and impactful research in related fields.