New mass estimates for massive binary systems: a probabilistic approach using polarimetric radiative transfer
Andrew G. Fullard, John T. O'Brien, Wolfgang E. Kerzendorf, Manisha, Shrestha, Jennifer L. Hoffman, Richard Ignace, and Patrick van der Smagt

TL;DR
This paper presents a probabilistic method using polarimetric radiative transfer and neural networks to accurately estimate the masses of Wolf-Rayet binary systems, improving understanding of their evolution and potential as gravitational wave sources.
Contribution
It introduces a neural network-accelerated Monte Carlo radiative transfer model for Bayesian mass estimation of Wolf-Rayet binaries using polarization data, addressing biases in analytic models.
Findings
Mass estimates for three systems are refined and constrained.
Disagreement with previous mass estimates for WR 153.
Highlights the importance of polarization observations for binary mass determination.
Abstract
Understanding the evolution of massive binary stars requires accurate estimates of their masses. This understanding is critically important because massive star evolution can potentially lead to gravitational wave sources such as binary black holes or neutron stars. For Wolf-Rayet stars with optically thick stellar winds, their masses can only be determined with accurate inclination angle estimates from binary systems which have spectroscopic measurements. Orbitally-phased polarization signals can encode the inclination angle of binary systems, where the Wolf-Rayet winds act as scattering regions. We investigated four Wolf-Rayet + O star binary systems, WR 42, WR 79, WR 127, and WR 153, with publicly available phased polarization data to estimate their masses. To avoid the biases present in analytic models of polarization while retaining computational expediency, we used a…
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