A Mock Data and Science Challenge for Detecting an Astrophysical Stochastic Gravitational-Wave Background with Advanced LIGO and Advanced Virgo
Duncan Meacher, Michael Coughlin, Sean Morris, Tania Regimbau, Nelson, Christensen, Shivaraj Kandhasamy, Vuk Mandic, Joseph D. Romano, Eric Thrane

TL;DR
This paper presents a mock data challenge to test and prepare data analysis methods for detecting an astrophysical stochastic gravitational-wave background with Advanced LIGO and Virgo, demonstrating readiness for future detections.
Contribution
It introduces realistic mock data sets and evaluates the ability of current pipelines to detect and estimate parameters of the gravitational-wave background.
Findings
Successful recovery of injected gravitational-wave background energy density within 2σ
Demonstrated that advanced LIGO and Virgo can detect the background within a few years
Validated data analysis pipelines for future astrophysical gravitational-wave background detection
Abstract
The purpose of this mock data and science challenge is to prepare the data analysis and science interpretation for the second generation of gravitational-wave experiments Advanced LIGO-Virgo in the search for a stochastic gravitational-wave background signal of astrophysical origin. Here we present a series of signal and data challenges, with increasing complexity, whose aim is to test the ability of current data analysis pipelines at detecting an astrophysically produced gravitational-wave background, test parameter estimation methods and interpret the results. We introduce the production of these mock data sets that includes a realistic observing scenario data set where we account for different sensitivities of the advanced detectors as they are continuously upgraded toward their design sensitivity. After analysing these with the standard isotropic cross-correlation pipeline we find…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Cosmology and Gravitation Theories · Gamma-ray bursts and supernovae
