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Electronic Population Reconstruction from Strong-Field-Modified Absorption Spectra with a Convolutional Neural Network

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Richter,  Daniel
Division Prof. Dr. Thomas Pfeifer, MPI for Nuclear Physics, Max Planck Society;

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Magunia,  Alexander       
Division Prof. Dr. Thomas Pfeifer, MPI for Nuclear Physics, Max Planck Society;

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Rebholz,  Marc       
Division Prof. Dr. Thomas Pfeifer, MPI for Nuclear Physics, Max Planck Society;

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Ott,  Christian       
Division Prof. Dr. Thomas Pfeifer, MPI for Nuclear Physics, Max Planck Society;

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Pfeifer,  Thomas       
Division Prof. Dr. Thomas Pfeifer, MPI for Nuclear Physics, Max Planck Society;

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引用

Richter, D., Magunia, A., Rebholz, M., Ott, C., & Pfeifer, T. (2024). Electronic Population Reconstruction from Strong-Field-Modified Absorption Spectra with a Convolutional Neural Network. Optics, 5(1), 88-100. doi:10.3390/opt5010007.


引用: https://hdl.handle.net/21.11116/0000-000F-1F43-4
要旨
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.