Issue |
A&A
Volume 675, July 2023
|
|
---|---|---|
Article Number | A191 | |
Number of page(s) | 11 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202346372 | |
Published online | 20 July 2023 |
iNNterpol: High-precision interpolation of stellar atmospheres with a deep neural network using a 1D convolutional auto encoder for feature extraction★
1
Instituto de Astrofísica de Canarias,
C/Vía Láctea s/n,
38205
La Laguna, Tenerife, Spain
e-mail: carlos.westendorp@iac.es; andres.asensio@iac.es; carlos.allende.prieto@iac.es
2
Departamento de Astrofísica, Universidad de La Laguna,
38206
La Laguna, Tenerife, Spain
Received:
10
March
2023
Accepted:
16
May
2023
Context. Given the widespread availability of grids of models for stellar atmospheres, it is necessary to recover intermediate atmospheric models by means of accurate techniques that go beyond simple linear interpolation and capture the intricacies of the data.
Aims. Our goal is to establish a reliable, precise, lightweight, and fast method for recovering stellar model atmospheres, that is to say the stratification of mass column, temperature, gas pressure, and electronic density with optical depth given any combination of the defining atmospheric specific parameters: metallicity, effective temperature, and surface gravity, as well as the abundances of other key chemical elements.
Methods. We employed a fully connected deep neural network which in turn uses a 1D convolutional auto-encoder to extract the nonlinearities of a grid using the ATLAS9 and MARCS model atmospheres.
Results. This new method we call iNNterpol effectively takes into account the nonlinearities in the relationships of the data as opposed to traditional machine-learning methods, such as the light gradient boosting method (LightGBM), that are repeatedly used for their speed in well-known competitions with reduced datasets. We show a higher precision with a convolutional auto-encoder than using principal component analysis as a feature extractor. We believe it constitutes a useful tool for generating fast and precise stellar model atmospheres, mitigating convergence issues, as well as a framework for future developments. The code and data for both training and direct interpolation are available online for full reproducibility and to serve as a practical starting point for other continuous 1D data in the field and elsewhere.
Key words: methods: data analysis / methods: numerical / stars: atmospheres / catalogs
© The Authors 2023
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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