Catching Super Massive Black Hole Binaries Without a Net

TL;DR

This paper introduces a computationally efficient method using Metropolis-Hastings sampling and simulated annealing to detect supermassive black hole binaries in LISA data, effectively distinguishing signals from noise and foreground sources.

Contribution

It presents a novel, less computationally intensive approach for detecting SMBH binaries in gravitational wave data, outperforming traditional grid-based methods.

Findings

Successfully recovered binary parameters in simulated data

Effective detection despite low signal-to-noise ratios

Operates without prior removal of foreground sources

Abstract

The gravitational wave signals from coalescing Supermassive Black Hole Binaries are prime targets for the Laser Interferometer Space Antenna (LISA). With optimal data processing techniques, the LISA observatory should be able to detect black hole mergers anywhere in the Universe. The challenge is to find ways to dig the signals out of a combination of instrument noise and the large foreground from stellar mass binaries in our own galaxy. The standard procedure of matched filtering against a grid of templates can be computationally prohibitive, especially when the black holes are spinning or the mass ratio is large. Here we develop an alternative approach based on Metropolis-Hastings sampling and simulated annealing that is orders of magnitude cheaper than a grid search. We demonstrate our approach on simulated LISA data streams that contain the signals from binary systems of Schwarzschild Black Holes, embedded in instrument noise and a foreground containing 26 million galactic binaries. The search algorithm is able to accurately recover the 9 parameters that describe the black hole binary without first having to remove any of the bright foreground sources, even when the black hole system has low signal-to-noise.