The search for spinning black hole binaries in mock LISA data using a genetic algorithm

TL;DR

This paper presents an extended genetic algorithm for detecting and analyzing spinning black hole binaries in mock LISA data, successfully identifying sources and estimating key parameters despite waveform complexity.

Contribution

It introduces a multimodal genetic algorithm tailored for complex spinning black hole signals, improving detection and parameter estimation in LISA data analysis.

Findings

Successfully identified all five sources in MLDC 3.2

Recovered coalescence time, chirp mass, mass ratio, and sky location accurately

Detected multiple modes for spins and angular momentum with similar likelihoods

Abstract

Coalescing massive Black Hole binaries are the strongest and probably the most important gravitational wave sources in the LISA band. The spin and orbital precessions bring complexity in the waveform and make the likelihood surface richer in structure as compared to the non-spinning case. We introduce an extended multimodal genetic algorithm which utilizes the properties of the signal and the detector response function to analyze the data from the third round of mock LISA data challenge (MLDC 3.2). The performance of this method is comparable, if not better, to already existing algorithms. We have found all five sources present in MLDC 3.2 and recovered the coalescence time, chirp mass, mass ratio and sky location with reasonable accuracy. As for the orbital angular momentum and two spins of the Black Holes, we have found a large number of widely separated modes in the parameter space with similar maximum likelihood values.