Searching for Massive Black Hole Binaries in the first Mock LISA Data Challenge
Neil J. Cornish, Edward K. Porter

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.
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