Issue |
A&A
Volume 693, January 2025
|
|
---|---|---|
Article Number | A245 | |
Number of page(s) | 14 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202451348 | |
Published online | 22 January 2025 |
Search for hot subdwarf stars from SDSS images using a deep learning method: SwinBayesNet
1
School of Mathematics and Statistics, Shandong University,
Weihai,
264209
Shandong,
PR China
2
School of Mechanical, Electrical and Information Engineering, Shandong University,
Weihai,
264209
Shandong,
PR China
3
Key Laboratory of Stars and Interstellar Medium, Xiangtan University,
Xiangtan,
411105
Hunan,
PR China
★ Corresponding author; buyude@sdu.edu.cn
Received:
2
July
2024
Accepted:
30
November
2024
Hot subdwarfs are essential for understanding the structure and evolution of low-mass stars, binary systems, astroseismology, and atmospheric diffusion processes. In recent years, deep learning has driven significant progress in hot subdwarf searches. However, most approaches tend to focus on modelling with spectral data, which are inherently more costly and scarce compared to photometric data. To maximise the reliable candidates, we used Sloan Digital Sky Survey (SDSS) photometric images to construct a two-stage hot subdwarf search model called SwinBayesNet, which combines the Swin Transformer and Bayesian neural networks. This model not only provides classification results but also estimates uncertainty. As negative examples for the model, we selected five classes of stars prone to confusion with hot subdwarfs, including O-type stars, B-type stars, A-type stars, white dwarfs (WDs), and blue horizontal branch stars. On the test set, the two-stage model achieved F1 scores of 0.90 and 0.89 in the two-class and three-class classification stages, respectively. Subsequently, with the help of Gaia DR3, a large-scale candidate search was conducted in SDSS DR17. We found 6804 hot-subdwarf candidates, including 601 new discoveries. Based on this, we applied a model threshold of 0.95 and Bayesian uncertainty estimation for further screening, refining the candidates to 3413 high-confidence objects, which include 331 new discoveries.
Key words: methods: data analysis / methods: statistical / techniques: photometric / Hertzsprung-Russell and C-M diagrams / subdwarfs
© 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.
This article is published in open access under the Subscribe to Open model. Subscribe to A&A to support open access publication.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.