Catching Super Massive Black Hole Binaries Without a Net

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.

4 pages, 3 figures, Refined search algorithm, added low SNR example

References in corpus (2)

• MCMC Exploration of Supermassive Black Hole Binary Inspirals
• A Markov Chain Monte Carlo approach to the study of massive black hole binary systems with LISA

Cited by in corpus (14)

• A Solution to the Galactic Foreground Problem for LISA
• A Bayesian Approach to the Detection Problem in Gravitational Wave Astronomy
• The Effect of Higher Harmonic Corrections on the Detection of massive black hole binaries with LISA
• Coherent Bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors
• Building a stochastic template bank for detecting massive black hole binaries
• An algorithm for detection of extreme mass ratio inspirals in LISA data
• Subtraction-noise projection in gravitational-wave detector networks
• A Constrained Metropolis-Hastings Search for EMRIs in the Mock LISA Data Challenge 1B
• Improved time-frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data
• Extracting galactic binary signals from the first round of Mock LISA Data Challenges
• A Three-Stage Search for Supermassive Black Hole Binaries in LISA Data
• Detecting extreme mass ratio inspiral events in LISA data using the Hierarchical Algorithm for Clusters and Ridges (HACR)
• Searching for Massive Black Hole Binaries in the first Mock LISA Data Challenge
• Markov chain Monte Carlo searches for Galactic binaries in Mock LISA Data Challenge 1B data sets