Issue |
A&A
Volume 694, February 2025
|
|
---|---|---|
Article Number | A228 | |
Number of page(s) | 14 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202452224 | |
Published online | 18 February 2025 |
Breaking the degeneracy in stellar spectral classification from single wide-band images
1
Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM,
91191
Gif-sur-Yvette,
France
2
Institutes of Computer Science and Astrophysics, Foundation for Research and Technology Hellas (FORTH),
Greece
3
IRFU, CEA, Université Paris-Saclay,
91191
Gif-sur-Yvette,
France
4
IMT Atlantique, École Mines-Télécom,
Bretagne-Pays de la Loire,
France
★ Corresponding author; ezequiel.centofanti@cea.fr
Received:
12
September
2024
Accepted:
16
December
2024
The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of view. This is particularly important for weak gravitational lensing studies, where precise PSF modelling is essential to get accurate shear measurements. Previous research has demonstrated that the SED of stars can be inferred from low-resolution observations using machine-learning classification algorithms. However, a degeneracy exists between the PSF size, which can vary significantly across the field of view, and the spectral type of stars, leading to strong limitations of such methods. We propose a new SED classification method that incorporates stellar spectral information by using a preliminary PSF model, thereby breaking this degeneracy and enhancing the classification accuracy. Our method involves calculating a set of similarity features between an observed star and a preliminary PSF model at different wavelengths and applying a support vector machine to these similarity features to classify the observed star into a specific stellar class. The proposed approach achieves a 91% top-two accuracy, surpassing machine-learning methods that do not consider the spectral variation of the PSF. Additionally, we examined the impact of PSF modelling errors on the spectral classification accuracy.
Key words: techniques: image processing / stars: general
© The Authors 2025
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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