SCoRe: A New Framework to Study Unmodeled Physics from Gravitational Wave Data
Guillaume Dideron, Suvodip Mukherjee, Luis Lehner

TL;DR
SCoRe is a Bayesian data analysis framework designed to detect unmodeled physics in gravitational wave data by analyzing residual correlations, distinguishing true signals from noise, and applicable to current and future GW detectors.
Contribution
The paper introduces SCoRe, a novel Bayesian method that searches for unmodeled physics in GW data through structured residual correlation analysis.
Findings
Successfully applied to toy and model-based data
Can distinguish noise artifacts from true signals
Applicable to current and future GW detectors
Abstract
A confident discovery of physics beyond what has been consistently modeled from gravitational wave (GW) data requires a technique that can distinguish between noise artifacts and unmodeled signatures while also shedding light on the underlying physics. We propose a new data analysis method, \texttt{SCoRe} (Structured Correlated Residual), to search for unmodeled physics in the GW data which can cover both of these aspects. The method searches for structure in the cross-correlation power spectrum of the residual strain between pairs of GW detectors. It does so by projecting this power spectrum onto a frequency-dependent template. The template may be model-independent or model-dependent and is constructed based on the properties of the GW source parameters. The projection of the residual strain enables the distinction between noise artifacts and any true signal while capturing possible…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Geophysics and Gravity Measurements · Cosmology and Gravitation Theories
