Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware

Understand

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.