4.5 Article

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

Journal

MATTER AND RADIATION AT EXTREMES
Volume 8, Issue 3, Pages -

Publisher

AIP Publishing
DOI: 10.1063/5.0140828

Keywords

-

Ask authors/readers for more resources

The rapid development of ultrafast ultraintense laser technology provides opportunities for studying strong-field physics under extreme conditions. Accurate determination of laser pulse characteristics remains a challenge, especially for high laser powers. In this study, we propose a machine learning-assisted model that utilizes the radiative spin-flip effect to diagnose ultrafast ultraintense laser characteristics. The model accurately predicts pulse duration, peak intensity, and focal radius with relative errors of 0.1%-10%.
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 10(20)-10(22) W/cm(2). 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 assis-tance 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. (c) 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available