Kavli Affiliate: Salvatore Vitale
| First 5 Authors: , , , ,
| Summary:
The LIGO-Virgo-KAGRA catalog has been analyzed with an abundance of different
population models due to theoretical uncertainty in the formation of
gravitational-wave sources. To expedite model exploration, we introduce an
efficient and accurate variational Bayesian approach that learns the population
posterior with a normalizing flow and serves as a drop-in replacement for
existing samplers. With hardware acceleration, inference takes just seconds for
the current set of black-hole mergers and readily scales to larger catalogs.
The trained posteriors provide an arbitrary number of independent samples with
exact probability densities, unlike established stochastic sampling algorithms,
while requiring up to three orders of magnitude fewer likelihood evaluations
and as few as $mathcalO(10^3)$. Provided the posterior support is covered,
discrepancies can be addressed with smoothed importance sampling, which
quantifies a goodness-of-fit metric for the variational approximation while
also estimating the evidence for Bayesian model selection. Neural variational
inference thus enables interactive development, analysis, and comparison of
population models, making it a useful tool for astrophysical interpretation of
current and future gravitational-wave observations.
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