Fortifying the characterization of binary mergers in LIGO data

TL;DR

This paper enhances gravitational wave data analysis by jointly modeling instrument noise and astrophysical signals, leading to more reliable signal detection and characterization in LIGO data.

Contribution

It introduces a method to simultaneously estimate noise background and astrophysical signals, improving robustness of gravitational wave analysis.

Findings

Noise parameterization improves signal detection resilience.

Marginalizing noise uncertainty enhances Bayes factor consistency.

Method demonstrates effectiveness on LIGO data and simulations.

Abstract

The study of compact binary in-spirals and mergers with gravitational wave observatories amounts to optimizing a theoretical description of the data to best reproduce the true detector output. While most of the research effort in gravitational wave data modeling focuses on the gravitational wave- forms themselves, here we will begin to improve our model of the instrument noise by introducing parameters which allow us to determine the background instrumental power spectrum while simul- taneously characterizing the astrophysical signal. We use data from the fifth LIGO science run and simulated gravitational wave signals to demonstrate how the introduction of noise parameters results in resilience of the signal characterization to variations in an initial estimation of the noise power spectral density. We find substantial improvement in the consistency of Bayes factor calculations when we are able to marginalize over uncertainty in the instrument noise level.