Component Separation of a Isotropic Gravitational Wave Background

TL;DR

This paper introduces a simple, computationally efficient method for jointly estimating multiple spectral components of an isotropic gravitational wave background, improving the analysis of data from ground-based detectors.

Contribution

It presents a novel linear component separation technique that estimates multiple GWB spectral components simultaneously, unlike previous methods that considered one component at a time.

Findings

Method can jointly estimate multiple components.

Requires negligible computational resources.

Can determine the number of separable components with current data.

Abstract

A Gravitational Wave Background (GWB) is expected in the universe from the superposition of a large number of unresolved astrophysical sources and phenomena in the early universe. Each component of the background (e.g., from primordial metric perturbations, binary neutron stars, milli-second pulsars etc.) has its own spectral shape. Many ongoing experiments aim to probe GWB at a variety of frequency bands. In the last two decades, using data from ground-based laser interferometric gravitational wave (GW) observatories, upper limits on GWB were placed in the frequency range of ~50-1000 Hz, considering one spectral shape at a time. However, one strong component can significantly enhance the estimated strength of another component. Hence, estimation of the amplitudes of the components with different spectral shapes should be done jointly. Here we propose a method for "component separation" of a statistically isotropic background, that can, for the first time, jointly estimate the amplitudes of many components and place upper limits. The method is rather straightforward and needs negligible amount of computation. It utilises the linear relationship between the measurements and the amplitudes of the actual components, alleviating the need for a sampling based method, e.g., Markov Chain Monte Carlo (MCMC) or matched filtering, which are computationally intensive and cumbersome in a multi-dimensional parameter space. Using this formalism we could also study how many independent components can be separated using a given dataset from a network of current and upcoming ground based interferometric detectors.