The Search for Massive Black Hole Binaries with LISA
Neil J. Cornish, Edward K. Porter

TL;DR
This paper develops and compares advanced search algorithms for detecting massive black hole binaries with LISA, demonstrating improved speed, robustness, and accuracy in identifying multiple sources in complex data streams.
Contribution
Introduces a new frequency annealing search method and evaluates its performance against existing techniques for detecting MBHBs, including multiple sources.
Findings
Frequency annealing improves detection speed and robustness.
The 7-D MCMC effectively explores high-correlation parameter spaces.
The algorithm accurately recovers multiple binaries without contamination.
Abstract
In this work we focus on the search and detection of Massive black hole binary (MBHB) systems, including systems at high redshift. As well as expanding on previous works where we used a variant of Markov Chain Monte Carlo (MCMC), called Metropolis-Hastings Monte Carlo, with simulated annealing, we introduce a new search method based on frequency annealing which leads to a more rapid and robust detection. We compare the two search methods on systems where we do and do not see the merger of the black holes. In the non-merger case, we also examine the posterior distribution exploration using a 7-D MCMC algorithm. We demonstrate that this method is effective in dealing with the high correlations between parameters, has a higher acceptance rate than previously proposed methods and produces posterior distribution functions that are close to the prediction from the Fisher Information matrix.…
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