Efficient time-sampling method in Coulomb-corrected strong-field approximation

Control of the spider-like interference structure in photoelectron momentum distributions of a helium atom in a few-cycle nonlinear chirped laser pulse

We investigate theoretically the photoelectron momentum distributions (PMDs) of the helium atom in the few-cycle nonlinear chirped laser pulse. The numerical results show that the direction of the spider-like interference structure in PMDs exhibits periodic variations with the increase of the chirp parameter. It is illustrated that the direction of the spider-like interference structure is related to the direction of the electron motion by tracking the trajectories of the electrons. We also demonstrate that the carrier-envelope phase can precisely control the opening of the ionization channel. In addition, we investigate the PMDs when a chirp-free second harmonic (SH) laser pulse is added to the chirped laser field, the numerical results show that the interference patterns can change from only spider-like interference structure to both spider-like and ring-like interference structures.

Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics

Feynman's path integral approach is to sum over all possible spatiotemporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in the classical view. However, the complete characterization of the quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose a deep-learning-performed strong-field Feynman's formulation with a preclassification scheme that can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build a bridge between deep learning and strong-field physics through Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science and shed new light on the quantum-classical correspondence.

Proposal for Measuring Electron Displacement Induced by a Short Laser Pulse

In laser-matter interaction, most previous studies have focused on the change of the electron momentum induced by the external fields. Here, we theoretically investigate the electron displacement induced by an ultrashort pulse, whose precise waveform is hard to determine experimentally. We propose and numerically demonstrate a scheme to accurately measure the electron displacement using a ruler formed by the interfering spirals in the photoelectron momentum distribution generated by two oppositely circularly polarized pulses. The scheme is robust against the focusing volume effects and the jitter of the carrier envelope phase of the two circular pulses. The ability to measure the electron displacement by an arbitrary pulse may pave the way to quantitative control of the charge migration in matter on the scale of Ångström.