Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | L021102 |
| Fachzeitschrift | Physical Review A |
| Jahrgang | 105 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 7 Feb. 2022 |
Abstract
We use a convolutional neural network to retrieve the internuclear distance in the two-dimensional H2+ molecule ionized by a strong few-cycle laser pulse based on the photoelectron momentum distribution. We show that a neural network trained on a relatively small dataset consisting of a few thousand images can predict the internuclear distance with an absolute error less than 0.1 a.u. Deep learning allows us to retrieve more than one parameter from a given momentum distribution. Specifically, we used a convolutional neural network to retrieve both the internuclear distance and the laser intensity. We study the effect of focal averaging, and we find that the convolutional neural network trained using the focal averaged electron momentum distributions also shows a good performance in reconstructing the internuclear distance.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Atom- und Molekularphysik sowie Optik
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in: Physical Review A, Jahrgang 105, Nr. 2, L021102, 07.02.2022.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Deep learning for retrieval of the internuclear distance in a molecule from interference patterns in photoelectron momentum distributions
AU - Shvetsov-Shilovski, N. I.
AU - Lein, M.
N1 - Funding Information: We are grateful to S. Brennecke, F. Oppermann, and S. Yue for continued interest in this work and stimulating discussions. This work was supported by the Deutsche Forschungsgemeinschaft (Grant No. SH 1145/1-2).
PY - 2022/2/7
Y1 - 2022/2/7
N2 - We use a convolutional neural network to retrieve the internuclear distance in the two-dimensional H2+ molecule ionized by a strong few-cycle laser pulse based on the photoelectron momentum distribution. We show that a neural network trained on a relatively small dataset consisting of a few thousand images can predict the internuclear distance with an absolute error less than 0.1 a.u. Deep learning allows us to retrieve more than one parameter from a given momentum distribution. Specifically, we used a convolutional neural network to retrieve both the internuclear distance and the laser intensity. We study the effect of focal averaging, and we find that the convolutional neural network trained using the focal averaged electron momentum distributions also shows a good performance in reconstructing the internuclear distance.
AB - We use a convolutional neural network to retrieve the internuclear distance in the two-dimensional H2+ molecule ionized by a strong few-cycle laser pulse based on the photoelectron momentum distribution. We show that a neural network trained on a relatively small dataset consisting of a few thousand images can predict the internuclear distance with an absolute error less than 0.1 a.u. Deep learning allows us to retrieve more than one parameter from a given momentum distribution. Specifically, we used a convolutional neural network to retrieve both the internuclear distance and the laser intensity. We study the effect of focal averaging, and we find that the convolutional neural network trained using the focal averaged electron momentum distributions also shows a good performance in reconstructing the internuclear distance.
UR - http://www.scopus.com/inward/record.url?scp=85124653085&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2108.08057
DO - 10.48550/arXiv.2108.08057
M3 - Article
AN - SCOPUS:85124653085
VL - 105
JO - Physical Review A
JF - Physical Review A
SN - 2469-9926
IS - 2
M1 - L021102
ER -