Rapid likelihood free inference of compact binary coalescences using accelerated hardware

Abstract

We report a gravitational-wave parameter estimation algorithm, AMPLFI , based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe . We present details of our algorithm and optimizations done related to data-loading and pre-processing on accelerated hardware. We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has ${\sim}6$ million trainable parameters with training times ${\lesssim}24$ h. Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe + AMPLFI is able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of ${\sim}6$ s.

Keywords

• Likelihood-free Inference
• Self-supervised learning
• Gravitational waves
• Multi-messenger astronomy