Searching for Double-line Spectroscopic Binaries in the LAMOST Medium-resolution Spectroscopic Survey with Deep Learning
Double-line spectroscopic binaries (SB2s) are a vital class of spectroscopic binaries for studying star formation and evolution. Searching for SB2s has been a hot topic in astronomy. Although considerable efforts have been made with fruitful outcomes, limitations in automation and accuracy still per...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/acc94e |
| _version_ | 1797696422021169152 |
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| author | Zepeng Zheng Zhong Cao Hui Deng Ying Mei Lei Tan Feng Wang |
| author_facet | Zepeng Zheng Zhong Cao Hui Deng Ying Mei Lei Tan Feng Wang |
| author_sort | Zepeng Zheng |
| collection | DOAJ |
| description | Double-line spectroscopic binaries (SB2s) are a vital class of spectroscopic binaries for studying star formation and evolution. Searching for SB2s has been a hot topic in astronomy. Although considerable efforts have been made with fruitful outcomes, limitations in automation and accuracy still persist. In this study, we developed a convolutional neural network model to search for SB2 candidates in LAMOST medium-resolution survey (MRS) data release (DR) 9 v1.0 by detecting double peaks in the cross-correlation function (CCF). We first generated a large number of spectra of single stars and binaries using the iSpec spectral synthesis software. The CCFs of these synthesized spectra were then calculated to form our training set. To efficiently detect the peaks of the CCFs, we applied a Softmax function-based noise reduction method. After testing and validation, the model achieved an accuracy of 97.76% in the testing set and was validated for more than 90% of the sample in several published SB2 catalogs. Finally, by applying the model to examine approximately 1.59 million LAMOST-MRS DR9 spectra, we identified 728 candidate SB2s, including 281 newly discovered ones. |
| first_indexed | 2024-03-12T03:26:07Z |
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| id | doaj.art-98ad0d7389c143c9a2e1acbdc29dcb1c |
| institution | Directory Open Access Journal |
| issn | 0067-0049 |
| language | English |
| last_indexed | 2024-03-12T03:26:07Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
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| series | The Astrophysical Journal Supplement Series |
| spelling | doaj.art-98ad0d7389c143c9a2e1acbdc29dcb1c2023-09-03T13:37:27ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492023-01-0126621810.3847/1538-4365/acc94eSearching for Double-line Spectroscopic Binaries in the LAMOST Medium-resolution Spectroscopic Survey with Deep LearningZepeng Zheng0Zhong Cao1https://orcid.org/0000-0002-2301-8030Hui Deng2https://orcid.org/0000-0002-8765-3906Ying Mei3https://orcid.org/0000-0002-7960-9251Lei Tan4Feng Wang5https://orcid.org/0000-0002-9847-7805Center For Astrophysics, Guangzhou University , Guangzhou 510006, People's Republic of China ; zhongc@gzhu.edu.cn, fengwang@gzhu.edu.cn; Great Bay Center, National Astronomical Data Center , Guangzhou, Guangdong 510006, People's Republic of ChinaCenter For Astrophysics, Guangzhou University , Guangzhou 510006, People's Republic of China ; zhongc@gzhu.edu.cn, fengwang@gzhu.edu.cn; Great Bay Center, National Astronomical Data Center , Guangzhou, Guangdong 510006, People's Republic of China; The School of Electronics and Communication Engineering, Guangzhou University , Guangzhou 510006, People's Republic of China; Peng Cheng Laboratory , Shenzhen 518000, People's Republic of ChinaCenter For Astrophysics, Guangzhou University , Guangzhou 510006, People's Republic of China ; zhongc@gzhu.edu.cn, fengwang@gzhu.edu.cn; Great Bay Center, National Astronomical Data Center , Guangzhou, Guangdong 510006, People's Republic of China; Peng Cheng Laboratory , Shenzhen 518000, People's Republic of ChinaCenter For Astrophysics, Guangzhou University , Guangzhou 510006, People's Republic of China ; zhongc@gzhu.edu.cn, fengwang@gzhu.edu.cn; Great Bay Center, National Astronomical Data Center , Guangzhou, Guangdong 510006, People's Republic of China; Peng Cheng Laboratory , Shenzhen 518000, People's Republic of ChinaCenter For Astrophysics, Guangzhou University , Guangzhou 510006, People's Republic of China ; zhongc@gzhu.edu.cn, fengwang@gzhu.edu.cn; Great Bay Center, National Astronomical Data Center , Guangzhou, Guangdong 510006, People's Republic of ChinaCenter For Astrophysics, Guangzhou University , Guangzhou 510006, People's Republic of China ; zhongc@gzhu.edu.cn, fengwang@gzhu.edu.cn; Great Bay Center, National Astronomical Data Center , Guangzhou, Guangdong 510006, People's Republic of China; Peng Cheng Laboratory , Shenzhen 518000, People's Republic of ChinaDouble-line spectroscopic binaries (SB2s) are a vital class of spectroscopic binaries for studying star formation and evolution. Searching for SB2s has been a hot topic in astronomy. Although considerable efforts have been made with fruitful outcomes, limitations in automation and accuracy still persist. In this study, we developed a convolutional neural network model to search for SB2 candidates in LAMOST medium-resolution survey (MRS) data release (DR) 9 v1.0 by detecting double peaks in the cross-correlation function (CCF). We first generated a large number of spectra of single stars and binaries using the iSpec spectral synthesis software. The CCFs of these synthesized spectra were then calculated to form our training set. To efficiently detect the peaks of the CCFs, we applied a Softmax function-based noise reduction method. After testing and validation, the model achieved an accuracy of 97.76% in the testing set and was validated for more than 90% of the sample in several published SB2 catalogs. Finally, by applying the model to examine approximately 1.59 million LAMOST-MRS DR9 spectra, we identified 728 candidate SB2s, including 281 newly discovered ones.https://doi.org/10.3847/1538-4365/acc94eConvolutional neural networksSpectroscopic binary stars |
| spellingShingle | Zepeng Zheng Zhong Cao Hui Deng Ying Mei Lei Tan Feng Wang Searching for Double-line Spectroscopic Binaries in the LAMOST Medium-resolution Spectroscopic Survey with Deep Learning The Astrophysical Journal Supplement Series Convolutional neural networks Spectroscopic binary stars |
| title | Searching for Double-line Spectroscopic Binaries in the LAMOST Medium-resolution Spectroscopic Survey with Deep Learning |
| title_full | Searching for Double-line Spectroscopic Binaries in the LAMOST Medium-resolution Spectroscopic Survey with Deep Learning |
| title_fullStr | Searching for Double-line Spectroscopic Binaries in the LAMOST Medium-resolution Spectroscopic Survey with Deep Learning |
| title_full_unstemmed | Searching for Double-line Spectroscopic Binaries in the LAMOST Medium-resolution Spectroscopic Survey with Deep Learning |
| title_short | Searching for Double-line Spectroscopic Binaries in the LAMOST Medium-resolution Spectroscopic Survey with Deep Learning |
| title_sort | searching for double line spectroscopic binaries in the lamost medium resolution spectroscopic survey with deep learning |
| topic | Convolutional neural networks Spectroscopic binary stars |
| url | https://doi.org/10.3847/1538-4365/acc94e |
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