The Search for Massive Black Hole Binaries with LISA

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. Finally, after carrying out searches where there is only one binary in the data stream, we examine the case where two black hole binaries are present in the same data stream. We demonstrate that our search algorithm can accurately recover both binaries, and more importantly showing that we can safely extract the MBHB sources without contaminating the rest of the data stream.