| Autor: |
Daniel Richter, Alexander Magunia, Marc Rebholz, Christian Ott, Thomas Pfeifer |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Optics, Vol 5, Iss 1, Pp 88-100 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2673-3269 |
| DOI: |
10.3390/opt5010007 |
| Popis: |
We simulate ultrafast electronic transitions in an atom and corresponding absorption line changes with a numerical, few-level model, similar to previous work. In addition, a convolutional neural network (CNN) is employed for the first time to predict electronic state populations based on the simulated modifications of the absorption lines. We utilize a two-level and four-level system, as well as a variety of laser-pulse peak intensities and detunings, to account for different common scenarios of light–matter interaction. As a first step towards the use of CNNs for experimental absorption data in the future, we apply two different noise levels to the simulated input absorption data. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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