Inferring properties of the local white dwarf population in astrometric and photometric surveys

TL;DR

This paper presents a Bayesian hierarchical framework to infer properties of the white dwarf population from astrometric and photometric data, accounting for observational errors and selection effects, demonstrated on mock Gaia and SDSS data.

Contribution

It introduces a novel Bayesian method that infers white dwarf population characteristics using only photometric and astrometric data, including error and selection bias considerations.

Findings

Photometry constrains temperature, surface gravity, and type distributions.

Astrometry improves surface gravity distribution estimates.

Method can identify unresolved binary white dwarfs.

Abstract

The Gaia mission will provide precise astrometry for an unprecedented number of white dwarfs (WDs), encoding information on stellar evolution, Type Ia supernovae progenitor scenarios, and the star formation and dynamical history of the Milky Way. With such a large data set, it is possible to infer properties of the WD population using only astrometric and photometric information. We demonstrate a framework to accomplish this using a mock data set with SDSS ugriz photometry and Gaia astrometric information. Our technique utilises a Bayesian hierarchical model for inferring properties of a WD population while also taking into account all observational errors of individual objects, as well as selection and incompleteness effects. We demonstrate that photometry alone can constrain the WD population's distributions of temperature, surface gravity and phenomenological type, and that astrometric information significantly improves determination of the WD surface gravity distribution. We also discuss the possibility of identifying unresolved binary WDs using only photometric and astrometric information.