The Mass Distribution of Companions to Low-Mass White Dwarfs

TL;DR

This paper develops a hierarchical probabilistic model to infer the mass distribution of unseen companions to low-mass white dwarfs using orbital data, successfully recovering known parameters and constraining neutron star companion fractions.

Contribution

The paper introduces a novel hierarchical Bayesian approach to determine companion mass distributions from orbital data, applicable to low-mass white dwarfs and post-common-envelope binaries.

Findings

Successfully recovers input parameters from mock data.

Estimates the mean companion mass as 0.74 solar masses.

Constrains neutron star companion fraction to less than 16% at 68% confidence.

Abstract

Measuring the masses of companions to single-line spectroscopic binary stars is (in general) not possible because of the unknown orbital plane inclination. Even when the mass of the visible star can be measured, only a lower limit can be placed on the mass of the unseen companion. However, since these inclination angles should be isotropically distributed, for a large enough, unbiased sample, the companion mass distribution can be deconvolved from the distribution of observables. In this work, we construct a hierarchical probabilistic model to infer properties of unseen companion stars given observations of the orbital period and projected radial velocity of the primary star. We apply this model to three mock samples of low-mass white dwarfs (LMWDs, $M\lesssim0.45~\Msun$) and a sample of post-common-envelope binaries. We use a mixture of two Gaussians to model the WD and neutron star (NS) companion mass distributions. Our model successfully recovers the initial parameters of these test data sets. We then apply our model to 55 WDs in the extremely low-mass (ELM) WD Survey. Our maximum a posteriori model for the WD companion population has a mean mass $\mu_{\rm WD} = 0.74~\Msun$, with a standard deviation $\sigma_{\rm WD} = 0.24~\Msun$. Our model constrains the NS companion fraction $f_{\rm NS}$ to be $<$16\% at 68\% confidence. We make samples from the posterior distribution publicly available so that future observational efforts may compute the NS probability for newly discovered LMWDs.