Bonn 2025 – wissenschaftliches Programm
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MO: Fachverband Molekülphysik
MO 19: Interaction with Strong or Short Laser Pulses I (joint session A/MO)
MO 19.3: Vortrag
Mittwoch, 12. März 2025, 15:15–15:30, GrHS Mathe
Machine learning for retrieval of the time-dependent internuclear distance in a molecule from photoelectron momentum distributions: fully quantum mechanical approach — •Nikolay Shvetsov-Shilovski and Manfred Lein — Leibniz Universität Hannover
We use a neural network for retrieval of the time-varying bond length in a dissociating one-dimensional H2+ molecule based on photoelectron momentum distributions (PMDs) from strong-field ionization. In contrast to our previous study [1], the motion of the atomic nuclei is treated fully quantum mechanically, i.e., PMDs are obtained from the solution of the time-dependent Schrödinger equation for the wavefunction depending on both the electron coordinate and the internuclear distance. We show that the neural network can recognize the time-dependent bond length with a good accuracy. Therefore, machine learning can be applied for time-resolved molecular imaging.
[1] N. I. Shvetsov-Shilovski and M. Lein, J. Phys. B: At. Mol. Opt. Phys. 57, 06LT01 (2024).
Keywords: time-varying bond length; strong-field ionization; time-resolved molecular imaging; machine learning; time-resolved molecular imaging