Kavli Affiliate: Salvatore Vitale
| First 5 Authors: Matthew Mould, Noah E. Wolfe, Salvatore Vitale, ,
| 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
that otherwise match with Jensen-Shannon divergences below 0.1 nats in our
14-dimensional parameter space, while requiring up to three orders of magnitude
fewer likelihood evaluations and as few as $mathcal{O}(10^3)$. 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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