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

Chomet, H; Plesnik, S; Nicolae, DC; Dunham, J; Gover, L; Weaving, T; Figueira de Morisson Faria, C; (2022) Controlling quantum effects in enhanced strong-field ionisation with machine-learning techniques. Journal of Physics B: Atomic, Molecular and Optical Physics , 55 (24) , Article 245501. 10.1088/1361-6455/aca4b0. Green open access

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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.

Type: Article
Title: Controlling quantum effects in enhanced strong-field ionisation with machine-learning techniques
Open access status: An open access version is available from UCL Discovery
DOI: 10.1088/1361-6455/aca4b0
Publisher version: https://doi.org/10.1088/1361-6455/aca4b0
Language: English
Additional information: Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy
URI: https://discovery.ucl.ac.uk/id/eprint/10160380
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