Detecting a stochastic gravitational-wave background in the presence of correlated magnetic noise

TL;DR

This paper introduces a Bayesian method to distinguish true stochastic gravitational-wave backgrounds from correlated magnetic noise in detector data, enhancing detection reliability.

Contribution

It proposes a novel Bayesian approach using magnetometer data and parameterized coupling models to identify and mitigate correlated magnetic noise in SGWB searches.

Findings

Method effectively prevents false SGWB detections due to magnetic noise.

Can detect SGWB even with strong correlated magnetic noise, with some reduction in significance.

Using a three-detector network improves noise identification and characterization.

Abstract

A detection of the stochastic gravitational-wave background (SGWB) from unresolved compact binary coalescences could be made by Advanced LIGO and Advanced Virgo at their design sensitivities. However, it is possible for magnetic noise that is correlated between spatially separated ground-based detectors to mimic a SGWB signal. In this paper we propose a new method for detecting correlated magnetic noise and separating it from a true SGWB signal. A commonly discussed method for addressing correlated magnetic noise is coherent subtraction in the raw data using Wiener filtering. The method proposed here uses a parameterized model of the magnetometer-to-strain coupling functions, along with measurements from local magnetometers, to estimate the contribution of correlated noise to the traditional SGWB detection statistic. We then use Bayesian model selection to distinguish between models that include correlated magnetic noise and those with a SGWB. Realistic simulations are used to show that this method prevents a false SGWB detection due to correlated magnetic noise. We also demonstrate that it can be used for a detection of a SGWB in the presence of strong correlated magnetic noise, albeit with reduced significance compared to the case with no correlated noise. Finally, we discuss the advantages of using a global three-detector network for both identifying and characterizing correlated magnetic noise.