A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
White dwarfs (WDs) are the ultimate stage for approximately 97% of stars in the Milky Way and are crucial for studying stellar evolution and galaxy structure. Due to their small size and low luminosity, WDs are not easily observable. Traditional search methods mostly rely on analyzing photometric pa...
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/ad97b8 |
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| author | Jiangchuan Zhang Yude Bu Mengmeng Zhang Duo Xie Zhenping Yi |
| author_facet | Jiangchuan Zhang Yude Bu Mengmeng Zhang Duo Xie Zhenping Yi |
| author_sort | Jiangchuan Zhang |
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| description | White dwarfs (WDs) are the ultimate stage for approximately 97% of stars in the Milky Way and are crucial for studying stellar evolution and galaxy structure. Due to their small size and low luminosity, WDs are not easily observable. Traditional search methods mostly rely on analyzing photometric parameters, which need high-quality data. In recent years, machine learning has played a significant role in astronomical data mining, due to its speed, real time, and precision. However, we have identified two common issues. On the one hand, many studies are based on high-quality spectral data, while a large amount of image data remain underutilized. On the other hand, existing astronomical algorithms are essentially classification algorithms, with sample incompleteness being a critical weakness. In our study, we propose the WD Network (WDNet) algorithm, which is a new object detection algorithm that integrates multiple advanced technologies and can directly locate WDs in images. WDNet overcomes the degradation issue of WDs and detected 31,065 candidates in 80,448 images. The candidates exhibit a wide range of types, including DA, DB, DC, DQ, and DZ, with surface gravity within 7.8 dex ∼ 8.4 dex, effective temperatures within 10,000 K ∼ 56,000 K, colors within −1 < u − g < 1 and −0.8 < g − r < 0.4, and reduced proper motion within 20∼35 mag. In the future, WDNet will conduct large-scale searches using the Chinese Space Station Telescope and Sloan Digital Sky Survey V. |
| format | Article |
| id | doaj-art-6ac10e3939724ea89b4045143317d2b6 |
| institution | Kabale University |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-6ac10e3939724ea89b4045143317d2b62025-01-20T12:07:48ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0127625310.3847/1538-4365/ad97b8A Multiple-detection-heads Machine Learning Algorithm for Detecting White DwarfsJiangchuan Zhang0https://orcid.org/0009-0006-5523-3997Yude Bu1https://orcid.org/0000-0002-9474-4734Mengmeng Zhang2Duo Xie3Zhenping Yi4https://orcid.org/0000-0001-8590-4110School of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Mechanical , Electrical and Information Engineering, Shandong University, Weihai, 264209, Shandong, People’s Republic of China ; yizhenping@sdu.edu.cnWhite dwarfs (WDs) are the ultimate stage for approximately 97% of stars in the Milky Way and are crucial for studying stellar evolution and galaxy structure. Due to their small size and low luminosity, WDs are not easily observable. Traditional search methods mostly rely on analyzing photometric parameters, which need high-quality data. In recent years, machine learning has played a significant role in astronomical data mining, due to its speed, real time, and precision. However, we have identified two common issues. On the one hand, many studies are based on high-quality spectral data, while a large amount of image data remain underutilized. On the other hand, existing astronomical algorithms are essentially classification algorithms, with sample incompleteness being a critical weakness. In our study, we propose the WD Network (WDNet) algorithm, which is a new object detection algorithm that integrates multiple advanced technologies and can directly locate WDs in images. WDNet overcomes the degradation issue of WDs and detected 31,065 candidates in 80,448 images. The candidates exhibit a wide range of types, including DA, DB, DC, DQ, and DZ, with surface gravity within 7.8 dex ∼ 8.4 dex, effective temperatures within 10,000 K ∼ 56,000 K, colors within −1 < u − g < 1 and −0.8 < g − r < 0.4, and reduced proper motion within 20∼35 mag. In the future, WDNet will conduct large-scale searches using the Chinese Space Station Telescope and Sloan Digital Sky Survey V.https://doi.org/10.3847/1538-4365/ad97b8White dwarf starsAstronomy data analysisAstronomical object identificationAstrostatistics |
| spellingShingle | Jiangchuan Zhang Yude Bu Mengmeng Zhang Duo Xie Zhenping Yi A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs The Astrophysical Journal Supplement Series White dwarf stars Astronomy data analysis Astronomical object identification Astrostatistics |
| title | A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs |
| title_full | A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs |
| title_fullStr | A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs |
| title_full_unstemmed | A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs |
| title_short | A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs |
| title_sort | multiple detection heads machine learning algorithm for detecting white dwarfs |
| topic | White dwarf stars Astronomy data analysis Astronomical object identification Astrostatistics |
| url | https://doi.org/10.3847/1538-4365/ad97b8 |
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