Digging the population of compact binary mergers out of the noise

Abstract

Coalescing compact binaries emitting gravitational wave (GW) signals, as recently detected by the Advanced LIGO-Virgo network, constitute a population over the multi-dimensional space of component masses and spins, redshift, and other parameters. Characterizing this population is a major goal of GW observations and may be approached via parametric models. We demonstrate hierarchical inference for such models with a method that accounts for uncertainties in each binary merger's individual parameters, for mass-dependent selection effects, and also for the presence of a second population of candidate events caused by detector noise. Thus, the method is robust to potential biases from a contaminated sample and allows us to extract information from events that have a relatively small probability of astrophysical origin.

19 pages, 12 figures

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• Black Holes: The Next Generation -- Repeated Mergers in Dense Star Clusters and their Gravitational-Wave Properties
• Reconstructing phenomenological distributions of compact binaries via gravitational wave observations
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• Parallelized Inference for Gravitational-Wave Astronomy
• Inferring the properties of a population of compact binaries in presence of selection effects
• Binary Black Hole Mergers from LIGO/Virgo O1 and O2: Population Inference Combining Confident and Marginal Events
• Accelerating parameter inference with graphics processing units
• A self-consistent method to estimate the rate of compact binary coalescences with a Poisson mixture model
• Searching for ultralight bosons within spin measurements of a population of binary black hole mergers
• VAMANA: Modeling Binary Black Hole Population with Minimal Assumptions
• Searching for mass-spin correlations in the population of gravitational-wave events: the GWTC-3 case study
• Gravitational wave population inference with deep flow-based generative network
• Gravitational-wave inference in the catalog era: evolving priors and marginal events
• Gravitational-wave selection effects using neural-network classifiers
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• Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: a case study
• Inferring the population properties of binary black holes from unresolved gravitational waves
• The population of merging compact binaries inferred using gravitational waves through GWTC-3
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• Gravitational wave detection without boot straps: a Bayesian approach
• Unmodelled Clustering Methods for Gravitational Wave Populations of Compact Binary Mergers
• The astrophysical odds of GW151216
• Assessing gravitational-wave binary black hole candidates with Bayesian odds
• Can GW231123 have a stellar origin?
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• A Unified $p_{\mathrm{astro}}$ for Gravitational Waves: Consistently Combining Information from Multiple Search Pipelines
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• Reconstructing the LISA massive black hole binary population via iterative kernel density estimation
• Binary black hole population inference combining confident and marginal events from the $\mathtt{IAS}\text{-}\mathtt{HM}$ search pipeline
• Comparing astrophysical models to gravitational-wave data in the observable space
• Reducing systematic uncertainties in gravitational-wave population analyses by increasing the detection threshold
• Probing the peak of star formation with the stochastic background of binary black hole mergers