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

Understand

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.