Searching for Massive Black Hole Binaries in the first Mock LISA Data Challenge

TL;DR

This paper presents a Markov Chain Monte Carlo-based algorithm for detecting and characterizing massive black hole binaries in simulated LISA data, demonstrating rapid source localization and accurate parameter recovery.

Contribution

Introduces a novel MCMC-based search method with thermostated frequency annealing for efficient detection of black hole binaries in LISA data.

Findings

Successfully locates sources in simulated data

Accurately recovers source parameters

Provides reliable error estimates

Abstract

The Mock LISA Data Challenge is a worldwide effort to solve the LISA data analysis problem. We present here our results for the Massive Black Hole Binary (BBH) section of Round 1. Our results cover Challenge 1.2.1, where the coalescence of the binary is seen, and Challenge 1.2.2, where the coalescence occurs after the simulated observational period. The data stream is composed of Gaussian instrumental noise plus an unknown BBH waveform. Our search algorithm is based on a variant of the Markov Chain Monte Carlo method that uses Metropolis-Hastings sampling and thermostated frequency annealing. We present results from the training data sets and the blind data sets. We demonstrate that our algorithm is able to rapidly locate the sources, accurately recover the source parameters, and provide error estimates for the recovered parameters.