J/A+A/693/A245 Catalog of hot subdwarf candidates (Wu+, 2025)
Search for hot subdwarf stars from SDSS images using a deep learning method:
SwinBayesNet.
Wu H., Bu Y., Zhang J., Zhang M., Yi Z., Liu M., Kong X., Lei Z.
<Astron. Astrophys. 693, A245 (2025)>
=2025A&A...693A.245W 2025A&A...693A.245W (SIMBAD/NED BibCode)
ADC_Keywords: Stars, subdwarf ; Photometry, SDSS
Keywords: methods: data analysis - methods: statistical -
techniques: photometric - Hertzsprung-Russell and C-M diagrams -
subdwarfs
Abstract:
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
shown significant progress in hot subdwarf searches. However, most
approaches tend to focus on modeling with spectral data, which are
inherently more costly and scarce compared to photometric data. To
maximize the reliable candidates, this paper utilized Sloan Digital
Sky Survey (SDSS) photometric images to construct a two-stage hot
subdwarfs search model called SwinBayesNet, which combines the Swin
Transformer and Bayesian Neural Networks. This model not only provides
classification results but also estimates uncertainty. 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 (BHBs), were selected as negative examples for the model.
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
samples, which included 331 new discoveries.
Description:
Based on the SwinBayesNet method proposed in the paper, we present a
catalog of 6804 hot subdwarf candidates. Of these, 601 are newly
discovered objects, labeled as "Source: New". Additionally, 3413
candidates are classified as high-confidence, indicated by
"High_confidence: Yes".
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table9.dat 204 6804 6804 Catalog of hot subdwarf candidates
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See also:
I/355 : Gaia DR3 Part 1. Main source (Gaia Collaboration, 2022)
https://skyserver.sdss.org/DR17 : SDSS DR17 Home Page
Byte-by-byte Description of file: table9.dat
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Bytes Format Units Label Explanations
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1- 13 F13.9 deg RAdeg Right Ascension (J2000) (RA)
15- 27 F13.9 deg DEdeg Declination (J2000) (DEC)
29- 47 A19 --- SDSS objID in SDSS DR17 (SDSSDR17_objID)
49- 67 A19 --- GaiaDR3 source_id in Gaia DR3 (GaiaDR3sourceid)
69- 78 F10.8 --- 2prob Two-class classification predicted
probability (Two_probability)
80- 90 E11.5 --- 2episnorm Two-class classification epistemic
uncertainty (normalised) (Twoepistemicnorm)
92-102 F11.9 --- 2aleanorm Two-class classification aleatoric
uncertainty (normalised) (Twoaleatoricnorm)
104-114 E11.5 --- 2epissoft Two-class classification epistemic
uncertainty (soft) (Twoepistemicsoft)
116-126 F11.9 --- 2aleasoft Two-class classification aleatoric
uncertainty (soft) (Twoaleatoricsoft)
128-130 A3 --- 2rel Two-class classification reliability
(Two_reliability)
132-141 F10.8 --- 3prob Three-class classification predicted
probability (Three_probability)
143-153 E11.5 --- 3episnorm Three-class classification Epistemic
uncertainty (normalised) (Threeepistemicnorm)
155-165 F11.9 --- 3aleanorm Three-class classification aleatoric
uncertainty (normalised) (Threealeatoricnorm)
167-177 E11.5 --- 3epissoft Three-class classification epistemic
uncertainty (soft) (Threeepistemicsoft)
179-189 E11.5 --- 3aleasoft Three-class classification aleatoric
uncertainty (soft) (Threealeatoricsoft)
191-193 A3 --- 3rel Three-class classification Reliability
(Three_reliability)
195-200 A6 --- Source The source of candidates, Culpan or New (Source)
202-204 A3 --- Highconf [Yes No] Confidence status of candidates
(High_confidence)
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Acknowledgements:
Huili Wu, whl1156646283(at)163.com
(End) Patricia Vannier [CDS] 08-Dec-2024