A Machine-Learning Compositional Study of Exoplanetary Material Accreted Onto Five Helium-Atmosphere White Dwarfs with $\texttt{cecilia}$
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910935306731520 |
|---|---|
| author | Badenas-Agusti, Mariona Xu, Siyi Vanderburg, Andrew De, Kishalay Dufour, Patrick Rogers, Laura K. Hoyos, Susana Blouin, Simon Viaña, Javier Bonsor, Amy Zuckerman, Ben |
| author_facet | Badenas-Agusti, Mariona Xu, Siyi Vanderburg, Andrew De, Kishalay Dufour, Patrick Rogers, Laura K. Hoyos, Susana Blouin, Simon Viaña, Javier Bonsor, Amy Zuckerman, Ben |
| contents | We present the first application of the Machine Learning (ML) pipeline $\texttt{cecilia}$ to determine the physical parameters and photospheric composition of five metal-polluted He-atmosphere white dwarfs without well-characterised elemental abundances. To achieve this, we perform a joint and iterative Bayesian fit to their $\textit{SDSS}$ (R=2,000) and $\textit{Keck/ESI}$ (R=4,500) optical spectra, covering the wavelength range from about 3,800Å to 9,000Å. Our analysis measures the abundances of at least two $-$and up to six$-$ chemical elements in their atmospheres with a predictive accuracy similar to that of conventional WD analysis techniques ($\approx$0.20 dex). The white dwarfs with the largest number of detected heavy elements are SDSS J0859$+$5732 and SDSS J2311$-$0041, which simultaneously exhibit O, Mg, Si, Ca, and Fe in their $\textit{Keck/ESI}$ spectra. For all systems, we find that the bulk composition of their pollutants is largely consistent with those of primitive CI chondrites to within 1-2$σ$. We also find evidence of statistically significant ($>2σ$) oxygen excesses for SDSS J0859$+$5732 and SDSS J2311$-$0041, which could point to the accretion of oxygen-rich exoplanetary material. In the future, as wide-field astronomical surveys deliver millions of public WD spectra to the scientific community, $\texttt{cecilia}$ aspires to unlock population-wide studies of polluted WDs, therefore helping to improve our statistical knowledge of extrasolar compositions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Machine-Learning Compositional Study of Exoplanetary Material Accreted Onto Five Helium-Atmosphere White Dwarfs with $\texttt{cecilia}$ Badenas-Agusti, Mariona Xu, Siyi Vanderburg, Andrew De, Kishalay Dufour, Patrick Rogers, Laura K. Hoyos, Susana Blouin, Simon Viaña, Javier Bonsor, Amy Zuckerman, Ben Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Machine Learning We present the first application of the Machine Learning (ML) pipeline $\texttt{cecilia}$ to determine the physical parameters and photospheric composition of five metal-polluted He-atmosphere white dwarfs without well-characterised elemental abundances. To achieve this, we perform a joint and iterative Bayesian fit to their $\textit{SDSS}$ (R=2,000) and $\textit{Keck/ESI}$ (R=4,500) optical spectra, covering the wavelength range from about 3,800Å to 9,000Å. Our analysis measures the abundances of at least two $-$and up to six$-$ chemical elements in their atmospheres with a predictive accuracy similar to that of conventional WD analysis techniques ($\approx$0.20 dex). The white dwarfs with the largest number of detected heavy elements are SDSS J0859$+$5732 and SDSS J2311$-$0041, which simultaneously exhibit O, Mg, Si, Ca, and Fe in their $\textit{Keck/ESI}$ spectra. For all systems, we find that the bulk composition of their pollutants is largely consistent with those of primitive CI chondrites to within 1-2$σ$. We also find evidence of statistically significant ($>2σ$) oxygen excesses for SDSS J0859$+$5732 and SDSS J2311$-$0041, which could point to the accretion of oxygen-rich exoplanetary material. In the future, as wide-field astronomical surveys deliver millions of public WD spectra to the scientific community, $\texttt{cecilia}$ aspires to unlock population-wide studies of polluted WDs, therefore helping to improve our statistical knowledge of extrasolar compositions. |
| title | A Machine-Learning Compositional Study of Exoplanetary Material Accreted Onto Five Helium-Atmosphere White Dwarfs with $\texttt{cecilia}$ |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2505.06228 |