Swift sky localization of gravitational waves using deep learning seeded importance sampling

Abstract

Fast, highly accurate, and reliable inference of the sky origin of gravitational waves would enable real-time multi-messenger astronomy. Current Bayesian inference methodologies, although highly accurate and reliable, are slow. Deep learning models have shown themselves to be accurate and extremely fast for inference tasks on gravitational waves, but their output is inherently questionable due to the blackbox nature of neural networks. In this work, we join Bayesian inference and deep learning by applying importance sampling on an approximate posterior generated by a multi-headed convolutional neural network. The neural network parametrizes Von Mises-Fisher and Gaussian distributions for the sky coordinates and two masses for given simulated gravitational wave injections in the LIGO and Virgo detectors. We generate skymaps for unseen gravitational-wave events that highly resemble predictions generated using Bayesian inference in a few minutes. Furthermore, we can detect poor predictions from the neural network, and quickly flag them.

12 pages, 9 figures, 1 table

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• Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
• Normalizing flows as an avenue to study overlapping gravitational wave signals
• Parameter estimation methods for analyzing overlapping gravitational wave signals in the third-generation detector era
• Convolutional neural network for gravitational-wave early alert: Going down in frequency
• Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
• Extract non-Gaussian Features in Gravitational Wave Observation Data Using Self-Supervised Learning
• Sky localization and polarization mode reconstruction of gravitational waves from GW170104 and GW150914
• Graph-based Summary Statistics for Revealing the Stochastic Gravitational Wave Background in Pulsar Timing Arrays