The Mass Distribution of Stellar-Mass Black Holes

We perform a Bayesian analysis of the mass distribution of stellar-mass black holes using the observed masses of 15 low-mass X-ray binary systems undergoing Roche lobe overflow and five high-mass, wind-fed X-ray binary systems. Using Markov Chain Monte Carlo calculations, we model the mass distribution both parametrically---as a power law, exponential, gaussian, combination of two gaussians, or log-normal distribution---and non-parametrically---as histograms with varying numbers of bins. We provide confidence bounds on the shape of the mass distribution in the context of each model and compare the models with each other by calculating their relative Bayesian evidence as supported by the measurements, taking into account the number of degrees of freedom of each model. The mass distribution of the low-mass systems is best fit by a power-law, while the distribution of the combined sample is best fit by the exponential model. We examine the existence of a "gap" between the most massive neutron stars and the least massive black holes by considering the value, M_1%, of the 1% quantile from each black hole mass distribution as the lower bound of black hole masses. The best model (the power law) fitted to the low-mass systems has a distribution of lower-bounds with M_1% > 4.3 Msun with 90% confidence, while the best model (the exponential) fitted to all 20 systems has M_1% > 4.5 Msun with 90% confidence. We conclude that our sample of black hole masses provides strong evidence of a gap between the maximum neutron star mass and the lower bound on black hole masses. Our results on the low-mass sample are in qualitative agreement with those of Ozel, et al (2010).