Improved Population and EOS Joint Inference for Binary Neutron Star Systems
Abstract
The extreme environment within neutron stars presents the opportunity to probe the nuclear equation of state at high densities while studying the properties of these stellar remnants. Historically, the equation of state and neutron star mass distribution have been inferred separately, with the latter often simply assumed to be fixed in studies of the former, but the dependence of the equation of state on neutron star mass indicates they should be inferred together. In this work, we extend the generalized version of the popular RIFT algorithm known as Hyperpipe to interface with flexible, user-provided priors that will facilitate joint inference of equation of state and binary neutron star population hyperparameters. We demonstrate this framework's utility via application to a combination of several existing observations, particularly including massive galactic pulsars, double neutron star systems, millisecond X-ray pulsars, the gravitational wave event GW170817 and the nuclear symmetry energy, within the context of a widely-adopted parametric EOS family and a simple Gaussian population model. We recover parameters consistent with previous analyses of the EOS and binary neutron star population, finding the latter to fit a bivariate normal distribution with mean (μ1,μ2) = (1.39,1.27) M⊙ and width σ= 0.08 M⊙. Furthermore, we implement novel coordinate transformations in our pipeline, with which we have discovered that the ad hoc prior boundaries used for this EOS family may be too restrictive. We present results with relaxed yet still physical prior boundaries, noting vastly improved hyperparameter posteriors and modestly different equation of state inferences.
Related
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- Wide parameter-space O3 search for continuous gravitational waves from unknown neutron stars in binary systems
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BibTeX
@misc{ebiri2026improved,
title = {{Improved Population and EOS Joint Inference for Binary Neutron Star Systems}},
author = {Marc Ebiri and Richard O'Shaughnessy},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.39057},
eprint = {2609.39057},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.39057},
}
RIS
TY - UNPB
TI - Improved Population and EOS Joint Inference for Binary Neutron Star Systems
AU - Marc Ebiri
AU - Richard O'Shaughnessy
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.39057
UR - https://arxiv.org/abs/2609.39057
AB - The extreme environment within neutron stars presents the opportunity to probe the nuclear equation of state at high densities while studying the properties of these stellar remnants. Historically, the equation of state and neutron star mass distribution have been inferred separately, with the latter often simply assumed to be fixed in studies of the former, but the dependence of the equation of state on neutron star mass indicates they should be inferred together. In this work, we extend the generalized version of the popular RIFT algorithm known as Hyperpipe to interface with flexible, user-provided priors that will facilitate joint inference of equation of state and binary neutron star population hyperparameters. We demonstrate this framework's utility via application to a combination of several existing observations, particularly including massive galactic pulsars, double neutron star systems, millisecond X-ray pulsars, the gravitational wave event GW170817 and the nuclear symmetry energy, within the context of a widely-adopted parametric EOS family and a simple Gaussian population model. We recover parameters consistent with previous analyses of the EOS and binary neutron star population, finding the latter to fit a bivariate normal distribution with mean (μ_1,μ_2) = (1.39,1.27) M_⊙ and width σ= 0.08 M_⊙. Furthermore, we implement novel coordinate transformations in our pipeline, with which we have discovered that the ad hoc prior boundaries used for this EOS family may be too restrictive. We present results with relaxed yet still physical prior boundaries, noting vastly improved hyperparameter posteriors and modestly different equation of state inferences.
ER -