Efficient method for measuring the parameters encoded in a gravitational-wave signal

TL;DR

This paper introduces a computationally efficient Bayesian method for accurately estimating parameters from gravitational-wave signals by combining prior information and marginalization, reducing computational costs.

Contribution

It presents a novel grid-based Bayesian approach that maintains accuracy while significantly lowering computational expenses compared to traditional stochastic methods.

Findings

Achieves accurate posterior distributions with low computational cost

Demonstrates effectiveness on simulated gravitational-wave data

Bridges the gap between simple predictive techniques and complex Bayesian inference

Abstract

Once upon a time, predictions for the accuracy of inference on gravitational-wave signals relied on computationally inexpensive but often inaccurate techniques. Recently, the approach has shifted to actual inference on noisy signals with complex stochastic Bayesian methods, at the expense of significant computational cost. Here, we argue that it is often possible to have the best of both worlds: a Bayesian approach that incorporates prior information and correctly marginalizes over uninteresting parameters, providing accurate posterior probability distribution functions, but carried out on a simple grid at a low computational cost, comparable to the inexpensive predictive techniques.