Opacity predictions in plasmas under stellar conditions using deep learning
Abstract
The aim of this work is to predict the opacity of plasmas under stellar conditions. We focus on iron and nickel, as these elements have been extensively investigated both theoretically and experimentally. In certain regimes, notably under non-local thermodynamic equilibrium, calculating the spectral opacity can be computationally demanding. To mitigate this difficulty, we employ deep learning models to predict accurate opacities while achieving a substantial reduction in computational cost. Specifically, we develop and train a hybrid model that combines a convolutional neural network with a multilayer perceptron. The proposed approach provides both the spectral opacity over the 0--10,000 eV energy range, and the corresponding mean opacities for large sets of temperatures and mass densities, in close agreement with standard computational methods. The agreement is very satisfactory, except in the L- and M-shell energy ranges for nickel at high temperatures, where a very large number of emerging transitions leads to substantial discrepancies.
Versions
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BibTeX
@misc{benredjem2026opacity,
title = {{Opacity predictions in plasmas under stellar conditions using deep learning}},
author = {Djamel Benredjem and Jean-Christophe Pain},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13252},
eprint = {2609.13252},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13252},
}
RIS
TY - UNPB
TI - Opacity predictions in plasmas under stellar conditions using deep learning
AU - Djamel Benredjem
AU - Jean-Christophe Pain
PY - 2026
DA - 2026/09/05
PB - arXiv
DO - 10.48550/arxiv.2609.13252
UR - https://arxiv.org/abs/2609.13252
AB - The aim of this work is to predict the opacity of plasmas under stellar conditions. We focus on iron and nickel, as these elements have been extensively investigated both theoretically and experimentally. In certain regimes, notably under non-local thermodynamic equilibrium, calculating the spectral opacity can be computationally demanding. To mitigate this difficulty, we employ deep learning models to predict accurate opacities while achieving a substantial reduction in computational cost. Specifically, we develop and train a hybrid model that combines a convolutional neural network with a multilayer perceptron. The proposed approach provides both the spectral opacity over the 0--10,000 eV energy range, and the corresponding mean opacities for large sets of temperatures and mass densities, in close agreement with standard computational methods. The agreement is very satisfactory, except in the L- and M-shell energy ranges for nickel at high temperatures, where a very large number of emerging transitions leads to substantial discrepancies.
ER -