Accurate modeling and mitigation of overlapping signals and glitches in gravitational-wave data

Abstract

The increasing sensitivity of gravitational-wave detectors has brought about an increase in the rate of astrophysical signal detections as well as the rate of "glitches"; transient and non-Gaussian detector noise. Temporal overlap of signals and glitches in the detector presents a challenge for inference analyses that typically assume the presence of only Gaussian detector noise. In this study we perform an extensive exploration of the efficacy of a recently proposed method that models the glitch with sine-Gaussian wavelets while simultaneously modeling the signal with compact-binary waveform templates. We explore a wide range of glitch families and signal morphologies and demonstrate that the joint modeling of glitches and signals (with wavelets and templates respectively) can reliably separate the two. We find that the glitches that most affect parameter estimation are also the glitches that are well modeled by such wavelets due to their compact time-frequency signature. As a further test, we investigate the robustness of this analysis against waveform systematics like those arising from the exclusion of higher-order modes and spin-precession effects. Our analysis provides an estimate of the signal parameters; the glitch waveform to be subtracted from the data and an assessment of whether some detected excess power consists of a glitch, signal, or both. We analyze the low-significance triggers (191225_215715 and 200114_020818) and find that they are both consistent with glitches overlapping high-mass signals.

Subtracting glitches from gravitational-wave detector data during the third LIGO-Virgo observing run

“…As BayesWave uses an MCMC to marginalize over the parameters, the end result is a posterior distribution of time series of the glitch, g(t). To actually perform the glitch mitigation, we select a fair draw from the posterior [31] by randomly selecting a single sample from the posterior, and subtract it from the detector data. The timescale to model glitches and produce glitch-subtracted data using BayesWave is up to multiple days depending on the type of glitch that is being analysed [30].…”

“…Scattered light glitches are a known challenge for BayesWave. Recent investigations [31] have shown that in order to fully model scattered light glitches with BayesWave, low-SNR wavelets must be used. These investigations also find that wavelets at the required SNRs are strongly disfavoured when using priors similar to those used by the analyses discussed in this work.…”

“…As previously stated, the uncertainty in the model of a glitch overlapping a gravitational-wave signal introduces another potential source of uncertainty in the PE results. Although it is possible to sample over the glitch model as an additional component in the PE (as is possible with recent updates to BayesWave [19,31]), this was not done in the O3 analyses. The impact of accounting for the statistical uncertainties in the BayesWave glitch model while performing gravitational-wave PE is explored in [31].…”

“…Although it is possible to sample over the glitch model as an additional component in the PE (as is possible with recent updates to BayesWave [19,31]), this was not done in the O3 analyses. The impact of accounting for the statistical uncertainties in the BayesWave glitch model while performing gravitational-wave PE is explored in [31]. Instead of these methods, a single glitch model was subtracted from the data as a preprocessing step.…”

“…A wide variety of similar methods have been developed to model and subtract glitches. Many techniques have been developed to model glitches using gravitational-wave strain data alone, including modelled Bayesian approaches [19,[28][29][30][31][32] and some based on machine learning [33,34]. Likewise, techniques that utilize the large number of additional auxiliary sensors at each observatory to model sources of noise with analytic methods [35][36][37][38][39][40][41][42][43][44] and machinelearning approaches [45][46][47][48][49] have been proposed.…”