Parameter Estimation in Searches for the Stochastic Gravitational-Wave Background

TL;DR

This paper introduces a Bayesian framework for estimating parameters of the stochastic gravitational-wave background using LIGO data, enabling better source identification and comparison with individual binary coalescence observations.

Contribution

It presents a novel Bayesian method for parameter estimation of SGWB models, applied to real data, and demonstrates how to differentiate between various SGWB sources.

Findings

First simultaneous 95% confidence limits on multiple SGWB parameters

Sensitivity estimates for Advanced LIGO/Virgo detectors

Method to compare SGWB signals with individual binary coalescence observations

Abstract

The stochastic gravitational-wave background (SGWB) is expected to arise from the superposition of many independent and unresolved gravitational-wave signals of either cosmological or astrophysical origin. The spectral content of the SGWB carries signatures of the physics that generated it. We present a Bayesian framework for estimating the parameters associated with different SGWB models using data from gravitational-wave detectors. We apply this technique to recent results from LIGO to produce the first simultaneous 95% confidence level limits on multiple parameters in generic power-law SGWB models and in SGWB models of compact binary coalescences. We also estimate the sensitivity of the upcoming second-generation detectors such as Advanced LIGO/Virgo to these models and demonstrate how SGWB measurements can be combined and compared with observations of individual compact binary coalescences in order to build confidence in the origin of an observed SGWB signal. In doing so, we demonstrate a novel means of differentiating between different sources of the SGWB.