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  1. ABSTRACT

    Observations of gravitational waves emitted by merging compact binaries have provided tantalizing hints about stellar astrophysics, cosmology, and fundamental physics. However, the physical parameters describing the systems (mass, spin, distance) used to extract these inferences about the Universe are subject to large uncertainties. The most widely used method of performing these analyses requires performing many Monte Carlo integrals to marginalize over the uncertainty in the properties of the individual binaries and the survey selection bias. These Monte Carlo integrals are subject to fundamental statistical uncertainties. Previous treatments of this statistical uncertainty have focused on ensuring that the precision of the inferred inference is unaffected; however, these works have neglected the question of whether sufficient accuracy can also be achieved. In this work, we provide a practical exploration of the impact of uncertainty in our analyses and provide a suggested framework for verifying that astrophysical inferences made with the gravitational-wave transient catalogue are accurate. Applying our framework to models used by the LIGO–Virgo–KAGRA collaboration and in the wider literature, we find that Monte Carlo uncertainty in estimating the survey selection bias is the limiting factor in our ability to probe narrow population models and this will rapidly grow more problematic as the size of the observed population increases.

     
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  2. Abstract

    Gravitational-wave observations of binary neutron star mergers provide valuable information about neutron star structure and the equation of state of dense nuclear matter. Numerous methods have been proposed to analyze the population of observed neutron stars, and previous work has demonstrated the necessity of jointly fitting the astrophysical distribution and the equation of state in order to accurately constrain the equation of state. In this work, we introduce a new framework to simultaneously infer the distribution of binary neutron star masses and the nuclear equation of state using Gaussian mixture model density estimates, which mitigates some of the limitations previously used methods suffer from. Using our method, we reproduce previous projections for the expected precision of our joint mass distribution and equation-of-state inference with tens of observations. We also show that mismodeling the equation of state can bias our inference of the neutron star mass distribution. While we focus on neutron star masses and matter effects, our method is widely applicable to population inference problems.

     
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