Controlling quantum effects in enhanced strong-field ionisation with machine-learning techniques

TL;DR

This paper employs machine learning techniques to analyze quantum interference in strong-field ionization of diatomic molecules, identifying optimal conditions for controlled ionization and elucidating underlying phase-space mechanisms.

Contribution

It introduces the use of dimensionality reduction methods to explore parameter effects and control ionization processes in molecular systems under strong laser fields.

Findings

Optimal conditions for enhanced ionization identified

Controlled ionization achieved with two-colour fields

Hierarchy of parameters for ionization control established

Abstract

We study non-classical pathways and quantum interference in enhanced ionisation of diatomic molecules in strong laser fields using machine learning techniques. Quantum interference provides a bridge, which facilitates intramolecular population transfer. Its frequency is higher than that of the field, intrinsic to the system and depends on several factors, for instance the state of the initial wavepacket or the internuclear separation. Using dimensionality reduction techniques, namely t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), we investigate the effect of multiple parameters at once and find optimal conditions for enhanced ionisation in static fields, and controlled ionisation release for two-colour driving fields. This controlled ionisation manifests itself as a step-like behaviour in the time-dependent autocorrelation function. We explain the features encountered with phase-space arguments, and also establish a hierarchy of parameters for controlling ionisation via phase-space Wigner quasiprobability flows, such as specific coherent superpositions of states, electron localisation and internuclear-distance ranges.