MCMC Exploration of Supermassive Black Hole Binary Inspirals

TL;DR

This paper evaluates the effectiveness of MCMC methods in analyzing supermassive black hole binary signals for LISA, showing they produce reliable parameter estimates and compare well with Fisher matrix predictions.

Contribution

It demonstrates the application of MCMC techniques to SMBHB inspiral data analysis and compares their results with Fisher matrix predictions, highlighting their accuracy.

Findings

MCMC provides reliable parameter estimates for SMBHB signals.

Fisher matrix slightly overestimates errors in intrinsic parameters.

Excellent agreement between MCMC and Fisher matrix for extrinsic parameters.

Abstract

The Laser Interferometer Space Antenna will be able to detect the inspiral and merger of Super Massive Black Hole Binaries (SMBHBs) anywhere in the Universe. Standard matched filtering techniques can be used to detect and characterize these systems. Markov Chain Monte Carlo (MCMC) methods are ideally suited to this and other LISA data analysis problems as they are able to efficiently handle models with large dimensions. Here we compare the posterior parameter distributions derived by an MCMC algorithm with the distributions predicted by the Fisher information matrix. We find excellent agreement for the extrinsic parameters, while the Fisher matrix slightly overestimates errors in the intrinsic parameters.