Issue |
EPJ Web Conf.
Volume 309, 2024
EOS Annual Meeting (EOSAM 2024)
|
|
---|---|---|
Article Number | 15003 | |
Number of page(s) | 2 | |
Section | Focused Sessions (FS) 5- Machine-Learning for Optics and Photonic Computing for AI | |
DOI | https://doi.org/10.1051/epjconf/202430915003 | |
Published online | 31 October 2024 |
https://doi.org/10.1051/epjconf/202430915003
Integrating artificial intelligence into the simulation of structured laser-driven high harmonic generation
1 Grupo de Investigación en Aplicaciones del Láser y Fotónica, Universidad de Salamanca, Pl. Merced s/n, Salamanca E-37008, Spain
2 Department of Physics, Colorado School of Mines, 1523 Illinois Street, Golden, CO 80401, USA
3 Optoelectronics Research Centre, University of Southampton, Southampton SO17 1BJ, UK
4 Unidad de Excelencia en Luz y Materia Estructuradas (LUMES), Universidad de Salamanca, Pl. Merced s/n 37008 Salamanca, Spain
* e-mail: jmpablosm@usal.es
Published online: 31 October 2024
High harmonic generation (HHG) stands as one of the most complex processes in strong-field physics, as it enables the conversion of laser light from the infrared to the extreme-ultraviolet or even the soft x-rays, enabling the synthesis and control of pulses lasting as short as tens of attoseconds. Accurately simulating this nonlinear and non-perturbative phenomena requires the coupling the dynamics of laser-driven electronic wavepackets, described by the three-dimensional time-dependent Schrödinger equation (3D-TDSE), with macroscopic Maxwell’s equations. Such calculations are extremely demanding due to the duality of microscopic and macroscopic nature of the process, thereby requiring the use of approximations. We develop a HHG method assisted by artificial intelligence that facilitates the simulation of macroscopic HHG within the framework of 3D-TDSE. This approach is particularly suited to simulate HHG driven by structured laser pulses. In particular, we demonstrate a self-interference effect in HHG driven by Hermite-Gauss beams. The theoretical and experimental agreement allows us to validate the AI-based model, and to identify a unique signature of the quantum nature of the HHG process.
© The Authors, published by EDP Sciences, 2024
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