SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining
This paper proposes a spectral transformer encoder (SpecTE) model based on a denoising pretraining technique, and applies it to estimating radial velocity (RV) and three stellar atmospheric physical parameters together with 15 chemical element abundances from low-resolution spectra from the Large Sk...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | The Astrophysical Journal Supplement Series |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4365/adcf9b |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850110620448325632 |
|---|---|
| author | Xirong Zhao Xiangru Li Hui Li Xianqi Liu |
| author_facet | Xirong Zhao Xiangru Li Hui Li Xianqi Liu |
| author_sort | Xirong Zhao |
| collection | DOAJ |
| description | This paper proposes a spectral transformer encoder (SpecTE) model based on a denoising pretraining technique, and applies it to estimating radial velocity (RV) and three stellar atmospheric physical parameters together with 15 chemical element abundances from low-resolution spectra from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). SpecTE enhances its sensitivity to spectral features and improves its parameter estimation accuracy by prelearning a mapping from low-quality spectra to high-quality spectra. We systematically evaluate the performance of SpecTE on the test set. On the cases with a signal-to-noise ratio of g ≥ 5, SpecTE achieved mean absolute error of 45 K for T _eff , 0.08 dex for $\mathrm{log}\,g$ , 0.037–0.05 dex for Fe, Mg, Si, Ni, Ca, and C, 0.05–0.077 dex for Al, Mn, O, S, and K, 0.09–0.12 dex for Ti, N, and Cr, 0.15 dex for V, 0.21 dex for Na, and 3.87 km s ^−1 for RV, respectively. Compared with StarNet, StarGRUNet, and SHBoost, SpecTE demonstrates significant advantages in both estimation accuracy and precision. Nevertheless, SpecTE also exhibits certain limitations, including relatively lower accuracy in the abundances of Na, V, and Cr. In addition, it shows some systematic biases when applied to metal-poor stars and stars with extreme elemental abundances. Finally, we estimated the parameters and their uncertainties for 9.80 million LAMOST DR11 low-resolution spectra, thereby compiling the LAMOST-SpecTE catalog. Compared with the DD-Payne and StarGRUNet catalogs, LAMOST-SpecTE exhibits higher accuracy and robustness. The computed catalog demonstrates good consistency with APOGEE and GALAH. The estimated stellar catalog, code, pretrained model, and experimental data are released online for astronomical exploration and algorithm research. |
| format | Article |
| id | doaj-art-202f19ef8097409d8eb06eea08cd7cf6 |
| institution | OA Journals |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-202f19ef8097409d8eb06eea08cd7cf62025-08-20T02:37:48ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0127824110.3847/1538-4365/adcf9bSpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising PretrainingXirong Zhao0Xiangru Li1https://orcid.org/0000-0003-3182-6959Hui Li2Xianqi Liu3South China Normal University , Guangzhou, Guangdong Province, People’s Republic of China ; lixiangru@scnu.edu.cnSouth China Normal University , Guangzhou, Guangdong Province, People’s Republic of China ; lixiangru@scnu.edu.cnSouth China Normal University , Guangzhou, Guangdong Province, People’s Republic of China ; lixiangru@scnu.edu.cnSouth China Normal University , Guangzhou, Guangdong Province, People’s Republic of China ; lixiangru@scnu.edu.cnThis paper proposes a spectral transformer encoder (SpecTE) model based on a denoising pretraining technique, and applies it to estimating radial velocity (RV) and three stellar atmospheric physical parameters together with 15 chemical element abundances from low-resolution spectra from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). SpecTE enhances its sensitivity to spectral features and improves its parameter estimation accuracy by prelearning a mapping from low-quality spectra to high-quality spectra. We systematically evaluate the performance of SpecTE on the test set. On the cases with a signal-to-noise ratio of g ≥ 5, SpecTE achieved mean absolute error of 45 K for T _eff , 0.08 dex for $\mathrm{log}\,g$ , 0.037–0.05 dex for Fe, Mg, Si, Ni, Ca, and C, 0.05–0.077 dex for Al, Mn, O, S, and K, 0.09–0.12 dex for Ti, N, and Cr, 0.15 dex for V, 0.21 dex for Na, and 3.87 km s ^−1 for RV, respectively. Compared with StarNet, StarGRUNet, and SHBoost, SpecTE demonstrates significant advantages in both estimation accuracy and precision. Nevertheless, SpecTE also exhibits certain limitations, including relatively lower accuracy in the abundances of Na, V, and Cr. In addition, it shows some systematic biases when applied to metal-poor stars and stars with extreme elemental abundances. Finally, we estimated the parameters and their uncertainties for 9.80 million LAMOST DR11 low-resolution spectra, thereby compiling the LAMOST-SpecTE catalog. Compared with the DD-Payne and StarGRUNet catalogs, LAMOST-SpecTE exhibits higher accuracy and robustness. The computed catalog demonstrates good consistency with APOGEE and GALAH. The estimated stellar catalog, code, pretrained model, and experimental data are released online for astronomical exploration and algorithm research.https://doi.org/10.3847/1538-4365/adcf9bAstronomy databases |
| spellingShingle | Xirong Zhao Xiangru Li Hui Li Xianqi Liu SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining The Astrophysical Journal Supplement Series Astronomy databases |
| title | SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining |
| title_full | SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining |
| title_fullStr | SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining |
| title_full_unstemmed | SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining |
| title_short | SpecTE: Parameter Estimation for LAMOST Low-resolution Stellar Spectra Based on Denoising Pretraining |
| title_sort | specte parameter estimation for lamost low resolution stellar spectra based on denoising pretraining |
| topic | Astronomy databases |
| url | https://doi.org/10.3847/1538-4365/adcf9b |
| work_keys_str_mv | AT xirongzhao specteparameterestimationforlamostlowresolutionstellarspectrabasedondenoisingpretraining AT xiangruli specteparameterestimationforlamostlowresolutionstellarspectrabasedondenoisingpretraining AT huili specteparameterestimationforlamostlowresolutionstellarspectrabasedondenoisingpretraining AT xianqiliu specteparameterestimationforlamostlowresolutionstellarspectrabasedondenoisingpretraining |