Growing Pains: Understanding the Impact of Likelihood Uncertainty on Hierarchical Bayesian Inference for Gravitational-Wave Astronomy Article Swipe

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C. Talbot , Jacob Golomb · YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2304.06138

Observations of gravitational waves emitted by merging compact binaries have provided tantalising 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 marginalise 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 has focused on ensuring 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.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2304.06138

PDF

https://arxiv.org/pdf/2304.06138

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green

Cited By

2

Related Works

10

OpenAlex ID

https://openalex.org/W4365600755

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OpenAlex ID

https://openalex.org/W4365600755

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DOI

https://doi.org/10.48550/arxiv.2304.06138

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Title

Growing Pains: Understanding the Impact of Likelihood Uncertainty on Hierarchical Bayesian Inference for Gravitational-Wave Astronomy

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Type

preprint

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Language

en

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Publication year

2023

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Publication date

2023-04-12

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Authors

C. Talbot, Jacob Golomb

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Landing page

https://arxiv.org/abs/2304.06138

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https://arxiv.org/pdf/2304.06138

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Yes

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OA status

green

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https://arxiv.org/pdf/2304.06138

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Concepts

LIGO, Monte Carlo method, Gravitational wave, Physics, Cosmology, Population, Inference, Bayesian inference, Statistical inference, Bayesian probability, Statistical physics, Frequentist inference, Gravitation, Astrophysics, Computer science, Astronomy, Statistics, Artificial intelligence, Mathematics, Sociology, Demography

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Cited by

2

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2023: 2

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Related works (count)

10

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