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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.17196 |
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| _version_ | 1866911829355134976 |
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| author | Wang, Hai-Feng Carraro, Giovanni Li, Xin Li, Qi-Da Spina, Lorenzo Chen, Li Wang, Guan-Yu Deng, Li-Cai |
| author_facet | Wang, Hai-Feng Carraro, Giovanni Li, Xin Li, Qi-Da Spina, Lorenzo Chen, Li Wang, Guan-Yu Deng, Li-Cai |
| contents | In this study we estimate the stellar ages of LAMOST DR8 Red Giant Branch (RGB) stars based on the Gradient Boosting Decision Tree algorithm (GBDT). We used 2,643 RGB stars extracted from the APOKASC-2 astero-seismological catalog as training data-set. After selecting the parameterses ([$α$/Fe], [C/Fe], T$_{eff}$, [N/Fe], [C/H], log g) highly correlated with age using GBDT, we apply the same GBDT method to the new catalog of more than 590,000 stars classified as RGB stars. The test data-set shows that the median relative error is around 11.6$\%$ for the method. We also compare the predicted ages of RGB stars with other studies (e.g., based on APOGEE), and find systematic differences. The final uncertainty is about 15 to 30$\%$ compared to open clusters' ages. Then we present the spatial distribution of the RGB sample having an age determination, which could recreate the expected result, and discuss systematic biases. All these diagnostics show that one can apply the GBDT method to other stellar samples to estimate atmospheric parameters and age. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17196 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Age Determination of LAMOST Red Giant Branch stars based on the Gradient Boosting Decision Tree method Wang, Hai-Feng Carraro, Giovanni Li, Xin Li, Qi-Da Spina, Lorenzo Chen, Li Wang, Guan-Yu Deng, Li-Cai Solar and Stellar Astrophysics Astrophysics of Galaxies In this study we estimate the stellar ages of LAMOST DR8 Red Giant Branch (RGB) stars based on the Gradient Boosting Decision Tree algorithm (GBDT). We used 2,643 RGB stars extracted from the APOKASC-2 astero-seismological catalog as training data-set. After selecting the parameterses ([$α$/Fe], [C/Fe], T$_{eff}$, [N/Fe], [C/H], log g) highly correlated with age using GBDT, we apply the same GBDT method to the new catalog of more than 590,000 stars classified as RGB stars. The test data-set shows that the median relative error is around 11.6$\%$ for the method. We also compare the predicted ages of RGB stars with other studies (e.g., based on APOGEE), and find systematic differences. The final uncertainty is about 15 to 30$\%$ compared to open clusters' ages. Then we present the spatial distribution of the RGB sample having an age determination, which could recreate the expected result, and discuss systematic biases. All these diagnostics show that one can apply the GBDT method to other stellar samples to estimate atmospheric parameters and age. |
| title | Age Determination of LAMOST Red Giant Branch stars based on the Gradient Boosting Decision Tree method |
| topic | Solar and Stellar Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2310.17196 |