Coalescing Compact Binary Parameter Estimation with Gravitational Waves in the Presence of non-Gaussian Transient Noise

TL;DR

This paper investigates how non-Gaussian transient noise, or glitches, in gravitational-wave detector data bias the estimation of compact binary parameters, and proposes criteria for safe time separation to avoid such biases.

Contribution

It quantifies parameter estimation biases caused by common LIGO glitches and identifies safe time separation thresholds to mitigate bias without glitch subtraction.

Findings

Significant biases in mass, spin, and sky position due to glitches.

Glitches within the time prior to GW signals cause more extreme biases.

Most parameters are susceptible to bias from overlapping noise sources.

Abstract

Data from gravitational-wave (GW) detectors often contains a high rate of non-Gaussian transient noise, known as glitches. The parameters estimated from GW signals coinciding with detector glitches are occasionally biased away from their true values. During the first part of the fourth LIGO-Virgo-KAGRA (LVK) observing run, 29% of GW candidates had overlapping or nearby glitches in one or more detectors. In the latter part of the fourth observation run, sensitivity improvements have increased the rates of GW detection. Consequently, scenarios in which GW signals and detector glitches overlap in time are more likely. In this study, we quantify shifts in inferred posterior distributions for short-duration compact binary coalescence GW signals interacting with common LIGO glitches as a function of time between the signal merger time and the glitch. We find statistically significant biases in parameter estimation for mass, spin, and sky position for "blip", "thunder", and "fast-scattering" glitches. Using these results, we provide estimates of what parameters are most affected by overlapping noise sources, as well as what constitutes a "safe" time separation between a gravitational wave signal and a glitch, without requiring glitch subtraction for unbiased source property estimation. We find that in a majority of cases, all parameters are susceptible to significant bias due to glitch interference. Additionally, we find that glitches that occur within the time prior of the GW signal cause more extreme biases than glitches outside of the time prior.