Building a stochastic template bank for detecting massive black hole binaries

TL;DR

This paper introduces a stochastic template bank method for detecting massive black hole binaries with LISA, utilizing a Monte Carlo grid and F-statistic for initial detection and follow-up refinement.

Contribution

It presents a novel stochastic approach to constructing template banks for gravitational wave detection of massive black hole binaries.

Findings

Effective detection of gravitational waves in simulated data

Successful initial parameter estimation using the coarse template bank

Refinement of parameters with Metropolis-Hasting stochastic search

Abstract

Coalescence of two massive black holes is the strongest and most promising source for LISA. In fact, gravitational signal from the end of inspiral and merger will be detectable throughout the Universe. In this article we describe the first step in the two-step hierarchical search for gravitational wave signal from the inspiraling massive BH binaries. It is based on the routinely used in the ground base gravitational wave astronomy method of filtering the data through the bank of templates. However we use a novel Monte-Carlo based (stochastic) method to lay a grid in the parameter space, and we use the likelihood maximized analytically over some parameters, known as F-statistic, as a detection statistic. We build a coarse template bank to detect gravitational wave signals and to make preliminary parameter estimation. The best candidates will be followed up using Metropolis-Hasting stochastic search to refine the parameter estimation. We demonstrate the performance of the method by applying it to the Mock LISA data challenge 1B (training data set).