Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware

TL;DR

This paper introduces AMPLFI, a likelihood-free inference algorithm utilizing normalizing flows, optimized for real-time gravitational-wave parameter estimation on accelerated hardware, enabling rapid analysis of binary black-hole signals.

Contribution

The paper presents a novel real-time parameter estimation method for gravitational waves using likelihood-free inference with normalizing flows, optimized for hardware acceleration.

Findings

Achieved real-time inference with ~6 seconds latency.

Trained a model with ~6 million parameters in under 24 hours.

Successfully detected and estimated parameters of BBH signals in simulated LIGO-Virgo data.

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 $\lesssim 24$ hours. 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.