Electronic Population Reconstruction from Strong-Field-Modified Absorption Spectra with a Convolutional Neural Network
Abstract
:1. Introduction
2. Materials and Methods
2.1. Few-Level Systems for Simulations of Absorption Line Shape Changes
2.2. Convolutional Neutral Network for State Population Reconstruction
2.2.1. Convolutional Neural Network Architecture
2.2.2. Training Dataset Properties
2.2.3. Training Process
3. Results
3.1. Line Shape Changes and Population Reconstruction for the Two-Level System
3.2. Line Shape Changes and Population Reconstruction for the Four-Level System
4. Conclusions and Outlook
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Error | No Noise | 1% Noise | 3% Noise |
---|---|---|---|
MSE | 5.7 × 10−7 | 3.4 × 10−3 | 5.5 × 10−3 |
MAE | 4.1 × 10−4 | 1.5 × 10−2 | 3.3 × 10−2 |
Error | No Noise | 1% Noise | 3% Noise |
---|---|---|---|
MSE | 3.6 × 10−8 | 1.7 × 10−4 | 1.3 × 10−3 |
MAE | 1.2 × 10−4 | 4.4 × 10−3 | 1.4 × 10−2 |
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Richter, D.; Magunia, A.; Rebholz, M.; Ott, C.; Pfeifer, T. Electronic Population Reconstruction from Strong-Field-Modified Absorption Spectra with a Convolutional Neural Network. Optics 2024, 5, 88-100. https://doi.org/10.3390/opt5010007
Richter D, Magunia A, Rebholz M, Ott C, Pfeifer T. Electronic Population Reconstruction from Strong-Field-Modified Absorption Spectra with a Convolutional Neural Network. Optics. 2024; 5(1):88-100. https://doi.org/10.3390/opt5010007
Chicago/Turabian StyleRichter, Daniel, Alexander Magunia, Marc Rebholz, Christian Ott, and Thomas Pfeifer. 2024. "Electronic Population Reconstruction from Strong-Field-Modified Absorption Spectra with a Convolutional Neural Network" Optics 5, no. 1: 88-100. https://doi.org/10.3390/opt5010007