Use of the MultiNest algorithm for gravitational wave data analysis

TL;DR

This paper demonstrates the application of the MultiNest algorithm to gravitational wave data analysis, efficiently identifying multiple source signals and accurately estimating their parameters in simulated LISA data.

Contribution

It introduces the use of the MultiNest algorithm for gravitational wave data analysis, highlighting its efficiency and model-independence in complex likelihood landscapes.

Findings

Successfully identified all solution modes in simulated data

Recovered source parameters with high precision

Demonstrated rapid convergence in a complex data set

Abstract

We describe an application of the MultiNest algorithm to gravitational wave data analysis. MultiNest is a multimodal nested sampling algorithm designed to efficiently evaluate the Bayesian evidence and return posterior probability densities for likelihood surfaces containing multiple secondary modes. The algorithm employs a set of live points which are updated by partitioning the set into multiple overlapping ellipsoids and sampling uniformly from within them. This set of live points climbs up the likelihood surface through nested iso-likelihood contours and the evidence and posterior distributions can be recovered from the point set evolution. The algorithm is model-independent in the sense that the specific problem being tackled enters only through the likelihood computation, and does not change how the live point set is updated. In this paper, we consider the use of the algorithm for gravitational wave data analysis by searching a simulated LISA data set containing two non-spinning supermassive black hole binary signals. The algorithm is able to rapidly identify all the modes of the solution and recover the true parameters of the sources to high precision.