J/A+A/699/A3 Gaia 500-pc white dwarf population (Garcia-Zamora+, 2025)
A random forest spectral classification of the Gaia 500 pc white dwarf
population.
Garcia-Zamora E.M., Torres S., Rebassa-Mansergas A., Ferrer-Burjachs A.
<Astron. Astrophys. 699, A3 (2025)>
=2025A&A...699A...3G 2025A&A...699A...3G (SIMBAD/NED BibCode)
ADC_Keywords: Stars, nearby ; Stars, white dwarf ; MK spectral classification ;
Optical
Keywords: catalogs - stars: atmospheres - white dwarfs
Abstract:
The third Gaia Data Release has provided the astronomical community
with astrometric data of more than 1.8 billion sources, and low
resolution spectra for 220 million. Such a large amount of data is
difficult to handle by means of visual inspection. In the last years,
artificial intelligence and machine learning algorithms have started
to be applied in astronomy for data analysis and automatic
classification, with excellent results.
In this work, we present a spectral analysis of the Gaia white dwarf
population up to 500pc from the Sun based on artificial intelligence
algorithms to classify the sample into their main spectral types and
subtypes.
In order to classify the sample, which consists of 78920 white dwarfs
with available Gaia spectra, we have applied a Random Forest algorithm
to the Gaia spectral coefficients. We used the Montreal White Dwarf
Database of already labeled objects as our training sample. The
classified sample is compared with other already published catalogs
and with our own higher resolution Gran Telescopio Canarias (GTC)
spectra, enabling the construction of a golden sample of
well-classified objects.
The Random Forest spectral classification of the 500-pc white dwarf
population achieves an excellent global accuracy of 0.91 and an
F1-score of 0.88 for the DA versus non-DA classification. In addition,
we obtain a very high accuracy of 0.76 and a global F1-score of 0.62
for the non-DA subtype classification. In particular, our
classification shows an excellent recall for DAs, DBs and DCs (>90%),
and a very good precision (≥80%) for DQs, DZs and DOs.
Unfortunately, our algorithm does not perform well in correctly
classifying subtypes given the low resolution of the Gaia spectra.
The use of machine learning techniques, particularly the Random Forest
algorithm, has enabled us to spectrally classify 78920 white dwarfs -
an increase of 543.6% over those previously labeled - with reasonable
accuracy. Having an estimate of the spectral type for the vast
majority of white dwarfs up to 500pc provides the possibility of
making better estimates of cooling ages, star formation rates, and
stellar evolution processes, among other fundamental aspects for the
study of the white dwarf population.
Description:
This catalogue contains the predicted spectral type for all objects in
the 500pc sample, as well as their Gaia DR3 source ID, 2016 position,
parallaxes, colour and absolute magnitude.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table3.dat 127 93439 Catalog of predicted spectral type (DA,DB, DC,
DO, DQ or DZ) for all objects in the 500pc
sample
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See also:
I/355 : Gaia DR3 Part 1. Main source (Gaia Collaboration, 2022)
Byte-by-byte Description of file: table3.dat
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Bytes Format Units Label Explanations
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1- 9 A9 --- --- [Gaia EDR3]
11- 29 I19 --- GaiaEDR3 Object Gaia source ID
31- 50 F20.16 deg RAdeg Right ascension (ICRS) at Ep=2016.0
52- 70 E19.13 deg DEdeg Declination (ICRS) at Ep=2016.0
72- 88 F17.13 mas plx Parallax
90-104 E15.8 mag BP-RP BP-RP colour
106-124 F19.16 mag GMAG Absolute magnitude
126-127 A2 --- SPPred Predicted spectral type of object (1)
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Note (1): DA, DB, DC, DO, DQ or DZ.
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Acknowledgements:
Enrique Miguel Garcia Zamora, enrique.miguel.garcia(at)upc.edu
(End) Patricia Vannier [CDS] 11-May-2025