Gravitational wave populations and cosmology with neural posterior estimation

Abstract

We apply neural posterior estimation for fast-and-accurate hierarchical Bayesian inference of gravitational wave populations. We use a normalizing flow to estimate directly the population hyper-parameters from a collection of individual source observations. This approach provides complete freedom in event representation, automatic inclusion of selection effects, and (in contrast to likelihood estimation) without the need for stochastic samplers to obtain posterior samples. Since the number of events may be unknown when the network is trained, we split into sub-population analyses that we later recombine; this allows for fast sequential analyses as additional events are observed. We demonstrate our method on a toy problem of dark siren cosmology, and show that inference takes just a few minutes and scales to $\sim 600$ events before performance degrades. We argue that neural posterior estimation therefore represents a promising avenue for population inference with large numbers of events.

16 + 5 pages, 7 + 1 figures. Small changes according to the published version

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• Two-Step Procedure to Detect Cosmological Gravitational Wave Backgrounds with Next-Generation Terrestrial Gravitational-Wave Detectors
• Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization
• Improving the detection sensitivity to primordial stochastic gravitational waves with reduced astrophysical foregrounds. II. Subthreshold binary neutron stars
• Cosmic Cartography II: completing galaxy catalogs for gravitational-wave cosmology
• Simulation-based population inference of LISA's Galactic binaries: Bypassing the global fit
• Fast and accurate parameter estimation of high-redshift sources with the Einstein Telescope
• Comparing astrophysical models to gravitational-wave data in the observable space
• Reducing systematic uncertainties in gravitational-wave population analyses by increasing the detection threshold
• The impact of precession and higher-order multipoles for gravitational wave cosmological inference
• Measurement prospects for the pair-instability mass cutoff with gravitational waves
• labrador: A domain-optimized machine-learning tool for gravitational wave inference
• Lightweight posterior construction for gravitational-wave catalogs with the Kolmogorov-Arnold network
• Sequence modeling of higher-order wave modes of binary black hole mergers
• Parameter inference of millilensed gravitational waves using neural spline flows