A Self-consistent Data-driven Model for Determining Stellar Parameters from Optical and Near-infrared Spectra
Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to data in one wavelength regime or another. For example, APOGEE...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
|
| Series: | The Astronomical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-3881/ad291d |
| _version_ | 1797248759532355584 |
|---|---|
| author | Logan Sizemore Diego Llanes Marina Kounkel Brian Hutchinson Keivan G. Stassun Vedant Chandra |
| author_facet | Logan Sizemore Diego Llanes Marina Kounkel Brian Hutchinson Keivan G. Stassun Vedant Chandra |
| author_sort | Logan Sizemore |
| collection | DOAJ |
| description | Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to data in one wavelength regime or another. For example, APOGEE Net has been applied to near-IR spectra from the Sloan Digital Sky Survey (SDSS)–V APOGEE survey to predict stellar parameters ( T _eff , log g , and [Fe/H]) for all stars with T _eff from 3000 to 50,000 K, including pre-main-sequence stars, OB stars, main-sequence dwarfs, and red giants. The increasing number of large surveys across multiple wavelength regimes provides the opportunity to improve data-driven models through learning from multiple data sets at once. In SDSS-V, a number of spectra of stars will be observed not just with APOGEE in the near-IR, but also with BOSS in the optical regime. Here, we aim to develop a complementary model, BOSS Net, that will replicate the performance of APOGEE Net in these optical data through label transfer. We further improve the model by extending it to brown dwarfs, as well as white dwarfs, resulting in a comprehensive coverage between 1700 < T _eff < 100,000 K and 0 < $\mathrm{log}g$ < 10, to ensure BOSS Net can reliably measure parameters of most of the commonly observed objects within this parameter space. We also update APOGEE Net to achieve a comparable performance in the near-IR regime. The resulting models provide a robust tool for measuring stellar evolutionary states, and, in turn, enable characterization of the star-forming history of the Galaxy. |
| first_indexed | 2024-04-24T20:19:42Z |
| format | Article |
| id | doaj.art-936524515a284d16b0aa501b4dcc5bd0 |
| institution | Directory Open Access Journal |
| issn | 1538-3881 |
| language | English |
| last_indexed | 2024-04-24T20:19:42Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astronomical Journal |
| spelling | doaj.art-936524515a284d16b0aa501b4dcc5bd02024-03-22T10:18:34ZengIOP PublishingThe Astronomical Journal1538-38812024-01-01167417310.3847/1538-3881/ad291dA Self-consistent Data-driven Model for Determining Stellar Parameters from Optical and Near-infrared SpectraLogan Sizemore0Diego Llanes1Marina Kounkel2https://orcid.org/0000-0002-5365-1267Brian Hutchinson3https://orcid.org/0000-0002-5537-008XKeivan G. Stassun4https://orcid.org/0000-0002-3481-9052Vedant Chandra5https://orcid.org/0000-0002-0572-8012Computer Science Department, Western Washington University , 516 High St., Bellingham, WA 98225, USAComputer Science Department, Western Washington University , 516 High St., Bellingham, WA 98225, USADepartment of Physics and Astronomy, University of North Florida , 1 UNF Dr., Jacksonville, FL, 32224, USA; Department of Physics and Astronomy, Vanderbilt University , VU Station 1807, Nashville, TN 37235, USAComputer Science Department, Western Washington University , 516 High St., Bellingham, WA 98225, USA; Foundational Data Science Group, Pacific Northwest National Laboratory , 902 Battelle Blvd., Richland, WA 99354, USADepartment of Physics and Astronomy, Vanderbilt University , VU Station 1807, Nashville, TN 37235, USACenter for Astrophysics ∣ Harvard & Smithsonian , 60 Garden St., Cambridge, MA 02138, USAData-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to data in one wavelength regime or another. For example, APOGEE Net has been applied to near-IR spectra from the Sloan Digital Sky Survey (SDSS)–V APOGEE survey to predict stellar parameters ( T _eff , log g , and [Fe/H]) for all stars with T _eff from 3000 to 50,000 K, including pre-main-sequence stars, OB stars, main-sequence dwarfs, and red giants. The increasing number of large surveys across multiple wavelength regimes provides the opportunity to improve data-driven models through learning from multiple data sets at once. In SDSS-V, a number of spectra of stars will be observed not just with APOGEE in the near-IR, but also with BOSS in the optical regime. Here, we aim to develop a complementary model, BOSS Net, that will replicate the performance of APOGEE Net in these optical data through label transfer. We further improve the model by extending it to brown dwarfs, as well as white dwarfs, resulting in a comprehensive coverage between 1700 < T _eff < 100,000 K and 0 < $\mathrm{log}g$ < 10, to ensure BOSS Net can reliably measure parameters of most of the commonly observed objects within this parameter space. We also update APOGEE Net to achieve a comparable performance in the near-IR regime. The resulting models provide a robust tool for measuring stellar evolutionary states, and, in turn, enable characterization of the star-forming history of the Galaxy.https://doi.org/10.3847/1538-3881/ad291dSpectroscopyFundamental parameters of starsSurveys |
| spellingShingle | Logan Sizemore Diego Llanes Marina Kounkel Brian Hutchinson Keivan G. Stassun Vedant Chandra A Self-consistent Data-driven Model for Determining Stellar Parameters from Optical and Near-infrared Spectra The Astronomical Journal Spectroscopy Fundamental parameters of stars Surveys |
| title | A Self-consistent Data-driven Model for Determining Stellar Parameters from Optical and Near-infrared Spectra |
| title_full | A Self-consistent Data-driven Model for Determining Stellar Parameters from Optical and Near-infrared Spectra |
| title_fullStr | A Self-consistent Data-driven Model for Determining Stellar Parameters from Optical and Near-infrared Spectra |
| title_full_unstemmed | A Self-consistent Data-driven Model for Determining Stellar Parameters from Optical and Near-infrared Spectra |
| title_short | A Self-consistent Data-driven Model for Determining Stellar Parameters from Optical and Near-infrared Spectra |
| title_sort | self consistent data driven model for determining stellar parameters from optical and near infrared spectra |
| topic | Spectroscopy Fundamental parameters of stars Surveys |
| url | https://doi.org/10.3847/1538-3881/ad291d |
| work_keys_str_mv | AT logansizemore aselfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT diegollanes aselfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT marinakounkel aselfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT brianhutchinson aselfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT keivangstassun aselfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT vedantchandra aselfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT logansizemore selfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT diegollanes selfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT marinakounkel selfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT brianhutchinson selfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT keivangstassun selfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra AT vedantchandra selfconsistentdatadrivenmodelfordeterminingstellarparametersfromopticalandnearinfraredspectra |