The Mass Distribution of Stellar-Mass Black Holes
Will M. Farr, Niharika Sravan, Andrew Cantrell, Laura Kreidberg,, Charles D. Bailyn, Ilya Mandel, Vicky Kalogera

TL;DR
This study uses Bayesian methods to analyze the mass distribution of stellar-mass black holes, finding evidence for a mass gap between neutron stars and black holes based on observed binary systems.
Contribution
It introduces a comprehensive Bayesian framework for modeling black hole mass distributions using multiple parametric and non-parametric models, with model comparison and confidence bounds.
Findings
Power-law best fits low-mass systems
Exponential best fits combined sample
Strong evidence for a mass gap between neutron stars and black holes
Abstract
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…
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Taxonomy
TopicsAstrophysical Phenomena and Observations · Pulsars and Gravitational Waves Research · Statistical and numerical algorithms
