Likelihood smoothing using gravitational wave surrogate models

TL;DR

This paper introduces a method to smooth likelihood surfaces in gravitational wave data analysis using surrogate models, reducing complexity and aiding in the detection of the global maximum.

Contribution

It presents a practical likelihood transform technique with Gaussian smoothing and surrogate models, improving the search for gravitational wave signals.

Findings

Reduces likelihood surface complexity

Enhances global maximum detection

Applicable to various waveform models

Abstract

Likelihood surfaces in the parameter space of gravitational wave signals can contain many secondary maxima, which can prevent search algorithms from finding the global peak and correctly mapping the distribution. Traditional schemes to mitigate this problem maintain the number of secondary maxima and thus retain the possibility that the global maximum will remain undiscovered. By contrast, the recently proposed technique of likelihood transform can modify the structure of the likelihood surface to reduce its complexity. We present a practical method to carry out a likelihood transform using a Gaussian smoothing kernel, utilising gravitational wave surrogate models to perform the smoothing operation analytically. We demonstrate the approach with Newtonian and post-Newtonian waveform models for an inspiralling circular compact binary.