Gaia-DR2 has provided an unprecedented number of white dwarf candidates of our Galaxy. In particular, it is estimated that Gaia-DR2 has observed nearly 400000 of these objects and close to 18000 up to 100pc from the Sun. This large quantity of data requires a thorough analysis in order to uncover their main Galactic population properties, in particular the thin and thick disc and halo components. Taking advantage of recent developments in artificial intelligence techniques, we make use of a detailed Random Forest algorithm to analyse an 8D space (equatorial coordinates, parallax, proper motion components, and photometric magnitudes) of accurate data provided by Gaia-DR2 within 100pc from the Sun. With the aid of a thorough and robust population synthesis code, we simulated the different components of the Galactic white dwarf population to optimize the information extracted from the algorithm for disentangling the different population components. The algorithm is first tested in a known simulated sample achieving an accuracy of 85.3 per cent. Our methodology is thoroughly compared to standard methods based on kinematic criteria demonstrating that our algorithm substantially improves previous approaches. Once trained, the algorithm is then applied to the Gaia-DR2 100pc white dwarf sample, identifying 12227 thin disc, 1410 thick disc, and 95 halo white dwarf candidates, which represent a proportion of 74:25:1, respectively. Hence, the numerical spatial densities are (3.6+/-0.4)x10^-3^pc^-3^, (1.2+/-0.4)x10^-3^pc^-3^, and (4.8+/-0.4)x10^-5^pc^-3^ for the thin disc, thick disc, and halo components, respectively. The populations thus obtained represent the most complete and volume-limited samples to date of the different components of the Galactic white dwarf population.

Cone search capability for table J/MNRAS/485/5573/table3 (Halo white dwarf candidates within 100 pc identified by our Random Forest algorithm)

Cone search capability for table J/MNRAS/485/5573/g100pcwd (Gaia 100 pc White Dwarf sample)