| Issue |
A&A
Volume 679, November 2023
|
|
|---|---|---|
| Article Number | A127 | |
| Number of page(s) | 15 | |
| Section | Catalogs and data | |
| DOI | https://doi.org/10.1051/0004-6361/202347601 | |
| Published online | 29 November 2023 | |
White dwarf Random Forest classification through Gaia spectral coefficients★
1
Departament de Física, Universitat Politécnica de Catalunya,
c/Esteve Terrades 5,
08860
Castelldefels, Spain
e-mail: santiago.torres@upc.edu
2
Institute for Space Studies of Catalonia,
c/Gran Capità 2–4, Edif. Nexus 104,
08034
Barcelona, Spain
Received:
28
July
2023
Accepted:
27
September
2023
Context. The third data release of Gaia has provided approximately 220 million low resolution spectra. Among these, about 100 000 correspond to white dwarfs. The magnitude of this quantity of data precludes the possibility of performing spectral analysis and type determination by human inspection. In order to tackle this issue, we explore the possibility of utilising a machine learning approach, based on a Random Forest algorithm.
Aims. Our goal is to analyse the viability of the Random Forest algorithm for the spectral classification of the white dwarf population within 100 pc from the Sun, based on the Hermite coefficients of Gaia spectra.
Methods. We utilised the assigned spectral type from the Montreal White Dwarf Database for training and testing our Random Forest algorithm. Once validated, our algorithm model was applied to the rest of the unclassified white dwarfs within 100 pc. First, we started by classifying the two major spectral type groups of white dwarfs: hydrogen-rich (DA) and hydrogen-deficient (non-DA). Next, we explored the possibility of classifying the various spectral subtypes, including the secondary spectral types in some cases.
Results. Our Random Forest classification presented a very high recall (>80%) for DA and DB white dwarfs, and a very high precision (>90%) for DB, DQ, and DZ white dwarfs. As a result we have assigned a spectral type to 9446 previously unclassified white dwarfs: 4739 DAs, 76 DBs (60 of them DBAs), 4437 DCs, 132 DZs, and 62 DQs (nine of them DQpec).
Conclusions. Despite the low resolution of Gaia spectra, the Random Forest algorithm applied to the Gaia spectral coefficients proves to be a highly valuable tool for spectral classification.
Key words: white dwarfs / stars: atmospheres / Hertzsprung-Russell and C–M diagrams / catalogs
Full Table 3 is available at the CDS via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/679/A127
© The Authors 2023
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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