Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey
A small fraction of giants possess photospheric lithium (Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In order to better understand the Li enhancement behaviors, a...
| Main Authors: | , , , , , |
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| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astronomical Journal |
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| Online Access: | https://doi.org/10.3847/1538-3881/aca098 |
| _version_ | 1797695813565022208 |
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| author | Beichen Cai Xiaoming Kong Jianrong Shi Qi Gao Yude Bu Zhenping Yi |
| author_facet | Beichen Cai Xiaoming Kong Jianrong Shi Qi Gao Yude Bu Zhenping Yi |
| author_sort | Beichen Cai |
| collection | DOAJ |
| description | A small fraction of giants possess photospheric lithium (Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In order to better understand the Li enhancement behaviors, a large and homogeneous Li-rich giant sample is needed. In this study, we designed a modified convolutional neural network model called Coord-DenseNet to determine the A (Li) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) giant spectra. The precision is good on the test set: MAE = 0.15 dex, and σ = 0.21 dex. We used this model to predict the Li abundance of more than 900,000 LAMOST DR8 LRS giant spectra and identified 7768 Li-rich giants with Li abundances ranging from 2.0 to 5.4 dex, accounting for about 1.02% of all giants. We compared the Li abundance estimated by our work with those derived from high-resolution spectra. We found that the consistency was good if the overall deviation of 0.27 dex between them was not considered. The analysis shows that the difference is mainly due to the high A (Li) from the medium-resolution spectra in the training set. This sample of Li-rich giants dramatically expands the existing sample size of Li-rich giants and provides us with more samples to further study the formation and evolution of Li-rich giants. |
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| issn | 1538-3881 |
| language | English |
| last_indexed | 2024-03-12T03:17:37Z |
| publishDate | 2023-01-01 |
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| series | The Astronomical Journal |
| spelling | doaj.art-fd7fb82b5ee7429aa8859ca8c7923e0f2023-09-03T14:08:26ZengIOP PublishingThe Astronomical Journal1538-38812023-01-0116525210.3847/1538-3881/aca098Li-rich Giants Identified from LAMOST DR8 Low-resolution SurveyBeichen Cai0Xiaoming Kong1https://orcid.org/0000-0002-4764-4749Jianrong Shi2https://orcid.org/0000-0002-0349-7839Qi Gao3https://orcid.org/0000-0003-4972-0677Yude Bu4Zhenping Yi5https://orcid.org/0000-0001-8590-4110School of Mechanical, Electrical & Information Engineering, Shandong University , Weihai, 264209, Shandong, People's Republic of China ; xmkong@sdu.edu.cnSchool of Mechanical, Electrical & Information Engineering, Shandong University , Weihai, 264209, Shandong, People's Republic of China ; xmkong@sdu.edu.cnKey Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People's Republic of ChinaKey Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People's Republic of ChinaSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People's Republic of ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University , Weihai, 264209, Shandong, People's Republic of China ; xmkong@sdu.edu.cnA small fraction of giants possess photospheric lithium (Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In order to better understand the Li enhancement behaviors, a large and homogeneous Li-rich giant sample is needed. In this study, we designed a modified convolutional neural network model called Coord-DenseNet to determine the A (Li) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) giant spectra. The precision is good on the test set: MAE = 0.15 dex, and σ = 0.21 dex. We used this model to predict the Li abundance of more than 900,000 LAMOST DR8 LRS giant spectra and identified 7768 Li-rich giants with Li abundances ranging from 2.0 to 5.4 dex, accounting for about 1.02% of all giants. We compared the Li abundance estimated by our work with those derived from high-resolution spectra. We found that the consistency was good if the overall deviation of 0.27 dex between them was not considered. The analysis shows that the difference is mainly due to the high A (Li) from the medium-resolution spectra in the training set. This sample of Li-rich giants dramatically expands the existing sample size of Li-rich giants and provides us with more samples to further study the formation and evolution of Li-rich giants.https://doi.org/10.3847/1538-3881/aca098Chemical abundancesStellar abundancesChemically peculiar giant starsChemical enrichmentStellar evolution |
| spellingShingle | Beichen Cai Xiaoming Kong Jianrong Shi Qi Gao Yude Bu Zhenping Yi Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey The Astronomical Journal Chemical abundances Stellar abundances Chemically peculiar giant stars Chemical enrichment Stellar evolution |
| title | Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey |
| title_full | Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey |
| title_fullStr | Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey |
| title_full_unstemmed | Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey |
| title_short | Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey |
| title_sort | li rich giants identified from lamost dr8 low resolution survey |
| topic | Chemical abundances Stellar abundances Chemically peculiar giant stars Chemical enrichment Stellar evolution |
| url | https://doi.org/10.3847/1538-3881/aca098 |
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