Astrophysical Model Selection in Gravitational Wave Astronomy
Matthew Adams, Neil Cornish, Tyson Littenberg

TL;DR
This paper introduces a hierarchical Bayesian method to use multiple gravitational wave detections for constraining astrophysical population models, demonstrated on galactic white dwarf binaries with promising precision.
Contribution
It develops a novel hierarchical Bayesian framework for joint inference of source parameters and population models from multiple gravitational wave detections.
Findings
Can constrain population parameters to within a few percent
Future space-based detectors can significantly improve astrophysical model constraints
Method outperforms existing electromagnetic observational bounds
Abstract
Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a Hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We…
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