Controlling enhanced ionisation with machine learning
Héloise Chomet
PhD student at University College London, UK
[email protected]
Strong-field enhanced ionization consists of an increase in the ionization rate of an extended molecule. Studies looking at enhanced ionization in H2+ have shown that nonclassical pathways and quantum interference provide a ‘bridge’ which facilitates intramolecular population transfer. The frequency of such bridge is intrinsic to the system and depends on several factors. Here we will use the t-SNE, an unsupervised machine learning dimensionality reduction technique, to investigate the effect of multiple parameters simultaneously in enhanced ionisation. We will then finally use semiclassical phase-space arguments as well as Wigner quasi-probability distributions to determine the physical cause of such ionisation pattern [1].
[1] H Chomet, S Plesnik, C D Nicolae, J Dunham, L Gover, T Weaving, C Figueira de Morisson Faria "Controlling quantum effects in enhanced strong-field ionisation with machine-learning techniques" arXiv:2205.03176 (2022)
PhD student at University College London, UK
[email protected]
Strong-field enhanced ionization consists of an increase in the ionization rate of an extended molecule. Studies looking at enhanced ionization in H2+ have shown that nonclassical pathways and quantum interference provide a ‘bridge’ which facilitates intramolecular population transfer. The frequency of such bridge is intrinsic to the system and depends on several factors. Here we will use the t-SNE, an unsupervised machine learning dimensionality reduction technique, to investigate the effect of multiple parameters simultaneously in enhanced ionisation. We will then finally use semiclassical phase-space arguments as well as Wigner quasi-probability distributions to determine the physical cause of such ionisation pattern [1].
[1] H Chomet, S Plesnik, C D Nicolae, J Dunham, L Gover, T Weaving, C Figueira de Morisson Faria "Controlling quantum effects in enhanced strong-field ionisation with machine-learning techniques" arXiv:2205.03176 (2022)
