Inferring Stellar Parameters from Iodine-imprinted Keck/HIRES Spectra with Machine Learning
The properties of exoplanet host stars are traditionally characterized through a detailed forward-modeling analysis of high-resolution spectra. However, many exoplanet radial velocity surveys employ iodine-cell-calibrated spectrographs, such that the vast majority of spectra obtained include an impr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astrophysical Journal Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/2041-8213/ad1ceb |
| _version_ | 1797351737494863872 |
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| author | Jude Gussman Malena Rice |
| author_facet | Jude Gussman Malena Rice |
| author_sort | Jude Gussman |
| collection | DOAJ |
| description | The properties of exoplanet host stars are traditionally characterized through a detailed forward-modeling analysis of high-resolution spectra. However, many exoplanet radial velocity surveys employ iodine-cell-calibrated spectrographs, such that the vast majority of spectra obtained include an imprinted forest of iodine absorption lines. For surveys that use iodine cells, iodine-free “template” spectra must be separately obtained for precise stellar characterization. These template spectra often require extensive additional observing time to obtain, and they are not always feasible to obtain for faint stars. In this paper, we demonstrate that machine-learning methods can be applied to infer stellar parameters and chemical abundances from iodine-imprinted spectra with high accuracy and precision. The methods presented in this work are broadly applicable to any iodine-cell-calibrated spectrograph. We make publicly available our spectroscopic pipeline, the Cannon HIRES Iodine Pipeline, which derives stellar parameters and 15 chemical abundances from iodine-imprinted spectra of FGK stars and which has been set up for ease of use with Keck/HIRES spectra. Our proof of concept offers an efficient new avenue to rapidly estimate a large number of stellar parameters even in the absence of an iodine-free template spectrum. |
| first_indexed | 2024-03-08T13:05:51Z |
| format | Article |
| id | doaj.art-0ef2e803adc34386aaf1146c5955f2c6 |
| institution | Directory Open Access Journal |
| issn | 2041-8205 |
| language | English |
| last_indexed | 2024-03-08T13:05:51Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Letters |
| spelling | doaj.art-0ef2e803adc34386aaf1146c5955f2c62024-01-18T18:36:27ZengIOP PublishingThe Astrophysical Journal Letters2041-82052024-01-019611L2410.3847/2041-8213/ad1cebInferring Stellar Parameters from Iodine-imprinted Keck/HIRES Spectra with Machine LearningJude Gussman0https://orcid.org/0000-0003-4705-3006Malena Rice1https://orcid.org/0000-0002-7670-670XDepartment of Astronomy, Indiana University , Bloomington, IN 47405, USADepartment of Astronomy, Yale University , New Haven, CT 06511, USAThe properties of exoplanet host stars are traditionally characterized through a detailed forward-modeling analysis of high-resolution spectra. However, many exoplanet radial velocity surveys employ iodine-cell-calibrated spectrographs, such that the vast majority of spectra obtained include an imprinted forest of iodine absorption lines. For surveys that use iodine cells, iodine-free “template” spectra must be separately obtained for precise stellar characterization. These template spectra often require extensive additional observing time to obtain, and they are not always feasible to obtain for faint stars. In this paper, we demonstrate that machine-learning methods can be applied to infer stellar parameters and chemical abundances from iodine-imprinted spectra with high accuracy and precision. The methods presented in this work are broadly applicable to any iodine-cell-calibrated spectrograph. We make publicly available our spectroscopic pipeline, the Cannon HIRES Iodine Pipeline, which derives stellar parameters and 15 chemical abundances from iodine-imprinted spectra of FGK stars and which has been set up for ease of use with Keck/HIRES spectra. Our proof of concept offers an efficient new avenue to rapidly estimate a large number of stellar parameters even in the absence of an iodine-free template spectrum.https://doi.org/10.3847/2041-8213/ad1cebStellar classificationStellar spectral linesStellar spectral typesExoplanetsExoplanet catalogsHigh resolution spectroscopy |
| spellingShingle | Jude Gussman Malena Rice Inferring Stellar Parameters from Iodine-imprinted Keck/HIRES Spectra with Machine Learning The Astrophysical Journal Letters Stellar classification Stellar spectral lines Stellar spectral types Exoplanets Exoplanet catalogs High resolution spectroscopy |
| title | Inferring Stellar Parameters from Iodine-imprinted Keck/HIRES Spectra with Machine Learning |
| title_full | Inferring Stellar Parameters from Iodine-imprinted Keck/HIRES Spectra with Machine Learning |
| title_fullStr | Inferring Stellar Parameters from Iodine-imprinted Keck/HIRES Spectra with Machine Learning |
| title_full_unstemmed | Inferring Stellar Parameters from Iodine-imprinted Keck/HIRES Spectra with Machine Learning |
| title_short | Inferring Stellar Parameters from Iodine-imprinted Keck/HIRES Spectra with Machine Learning |
| title_sort | inferring stellar parameters from iodine imprinted keck hires spectra with machine learning |
| topic | Stellar classification Stellar spectral lines Stellar spectral types Exoplanets Exoplanet catalogs High resolution spectroscopy |
| url | https://doi.org/10.3847/2041-8213/ad1ceb |
| work_keys_str_mv | AT judegussman inferringstellarparametersfromiodineimprintedkeckhiresspectrawithmachinelearning AT malenarice inferringstellarparametersfromiodineimprintedkeckhiresspectrawithmachinelearning |