# Random Forest identification of the thin disk, thick disk and halo Gaia-DR2 white dwarf population

## TL;DR

This study employs a Random Forest algorithm to classify Gaia-DR2 white dwarf candidates into thin disk, thick disk, and halo populations, significantly improving previous kinematic methods and providing the most complete volume-limited samples to date.

## Contribution

The paper introduces a novel machine learning approach using Random Forests to accurately identify Galactic white dwarf populations from Gaia-DR2 data, surpassing traditional kinematic classification methods.

## Key findings

- Identified 12,227 thin disk, 1,410 thick disk, and 95 halo white dwarfs.
- Achieved 85.3% accuracy in simulated data classification.
- Provided the most complete volume-limited samples of Galactic white dwarf populations.

## Abstract

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 400,000 of these objects and close to 18,000 up to 100 pc 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 disk and halo components. Taking advantage of recent developments in artificial intelligence techniques, we make use of a detailed Random Forest algorithm to analyse an 8-dimensional space (equatorial coordinates, parallax, proper motion components and photometric magnitudes) of accurate data provided by Gaia-DR2 within 100 pc 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%. 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 100 pc white dwarf sample, identifying 12,227 thin disk, 1,410 thick disk and 95 halo white dwarf candidates, which represent a proportion of 74:25:1, respectively. Hence, the numerical spatial densities are $(3.6\pm0.4)\times10^{-3}\,{\rm pc^{-3}}$, $(1.2\pm0.4)\times10^{-3}\,{\rm pc^{-3}}$ and $(4.8\pm0.4)\times10^{-5}\,{\rm pc^{-3}}$ for the thin disk, thick disk 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.