Details
| Original language | English |
|---|---|
| Article number | 06LT01 |
| Number of pages | 8 |
| Journal | Journal of Physics B: Atomic, Molecular and Optical Physics |
| Volume | 57 |
| Issue number | 6 |
| Publication status | Published - 6 Mar 2024 |
Abstract
We apply deep learning for retrieval of the time-dependent bond length in the dissociating two-dimensional H+2 molecule using photoelectron momentum distributions. We consider a pump-probe scheme and calculate electron momentum distributions from strong-field ionization by treating the motion of the nuclei classically, semiclassically or quantum mechanically. A convolutional neural network trained on momentum distributions obtained at fixed internuclear distances retrieves the time-varying bond length with an absolute error of 0.2–0.3 a.u.
Keywords
- deep learning, photoelectron momentum distributions, strong-field ionization, time-dependent internuclear distance
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Physics and Astronomy(all)
- Condensed Matter Physics
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In: Journal of Physics B: Atomic, Molecular and Optical Physics, Vol. 57, No. 6, 06LT01, 06.03.2024.
Research output: Contribution to journal › Letter › Research › peer review
}
TY - JOUR
T1 - Convolutional neural network for retrieval of the time-dependent bond length in a molecule from photoelectron momentum distributions
AU - Shvetsov-Shilovski, N. I.
AU - Lein, M.
N1 - Funding Information: This work was supported by the Deutsche Forschungsgemeinschaft (Project No. 336041027).
PY - 2024/3/6
Y1 - 2024/3/6
N2 - We apply deep learning for retrieval of the time-dependent bond length in the dissociating two-dimensional H+2 molecule using photoelectron momentum distributions. We consider a pump-probe scheme and calculate electron momentum distributions from strong-field ionization by treating the motion of the nuclei classically, semiclassically or quantum mechanically. A convolutional neural network trained on momentum distributions obtained at fixed internuclear distances retrieves the time-varying bond length with an absolute error of 0.2–0.3 a.u.
AB - We apply deep learning for retrieval of the time-dependent bond length in the dissociating two-dimensional H+2 molecule using photoelectron momentum distributions. We consider a pump-probe scheme and calculate electron momentum distributions from strong-field ionization by treating the motion of the nuclei classically, semiclassically or quantum mechanically. A convolutional neural network trained on momentum distributions obtained at fixed internuclear distances retrieves the time-varying bond length with an absolute error of 0.2–0.3 a.u.
KW - deep learning
KW - photoelectron momentum distributions
KW - strong-field ionization
KW - time-dependent internuclear distance
UR - http://www.scopus.com/inward/record.url?scp=85187527598&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2312.14636
DO - 10.48550/arXiv.2312.14636
M3 - Letter
AN - SCOPUS:85187527598
VL - 57
JO - Journal of Physics B: Atomic, Molecular and Optical Physics
JF - Journal of Physics B: Atomic, Molecular and Optical Physics
SN - 0953-4075
IS - 6
M1 - 06LT01
ER -