Photometric constraints on white dwarfs and the identification of extreme objects

TL;DR

This paper demonstrates how photometric and astrometric data, combined with Bayesian methods, can reliably identify white dwarfs and infer their properties, effectively distinguishing them from other celestial objects and identifying extreme cases.

Contribution

It introduces a Bayesian approach to break degeneracies in photometric data, improving the identification and characterization of white dwarfs, especially outliers like halo and ultra-cool WDs.

Findings

Bayesian methods outperform simple best-fit approaches in WD identification.

Most halo WD candidates are likely thick disk WDs within photometric noise.

Reanalysis confirms ultra-cool WD candidates but questions halo WD candidates.

Abstract

It is possible to reliably identify white dwarfs (WDs) without recourse to spectra, instead using photometric and astrometric measurements to distinguish them from Main Sequence stars and quasars. WDs' colours can also be used to infer their intrinsic properties (effective temperature, surface gravity, etc.), but the results obtained must be interpreted with care. The difficulties stem from the existence of a solid angle degeneracy, as revealed by a full exploration of the likelihood, although this can be masked if a simple best-fit approach is used. Conversely, this degeneracy can be broken if a Bayesian approach is adopted, as it is then possible to utilise the prior information on the surface gravities of WDs implied by spectroscopic fitting. The benefits of such an approach are particularly strong when applied to outliers, such as the candidate halo and ultra-cool WDs identified by Vidrih et al. (2007). A reanalysis of these samples confirms their results for the latter sample but suggests that that most of the halo candidates are thick disk WDs in the tails of the photometric noise distribution.