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

TL;DR

This paper presents a machine-learning-assisted method using electron spin depolarization to accurately diagnose ultrafast ultraintense laser pulse characteristics, achieving high precision with minimal shots, advancing strong-field physics research.

Contribution

It introduces a novel diagnostic approach leveraging electron spin effects combined with machine learning to determine laser parameters with high accuracy, even for arbitrary pulse profiles.

Findings

Predicts pulse duration, intensity, and focal radius with 0.1%-10% error

Requires only three laser shots for accurate diagnosis

Demonstrates robustness against electron beam fluctuations

Abstract

Rapid development of ultrafast ultraintense laser technologies 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 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 the 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.