Glitches far from transient gravitational-wave events do not bias inference

TL;DR

This study quantifies when glitches in gravitational-wave detectors can be safely ignored for compact binary coalescence parameter estimation. Using BayesWaveCpp to simulate CBC signals and glitches, and employing glitch reweighting and Jensen–Shannon divergence, the authors map four time-frequency regimes of glitch–signal overlap. They show glitches completely outside the CBC prior time-frequency region (Region IV) do not bias inference, while glitches overlapping the prior (Region I) cause biases that require mitigation; glitches near or after the signal (Regions II–III) can bias only at sufficiently high SNR or with close proximity to merger. The findings imply that, in practice, glitches with $SNR<50$ can be left unmitigated in many high-mass CBC analyses, substantially reducing computational and human effort, while rare, very loud glitches or close-in-time overlaps still warrant mitigation. The work provides concrete guidelines for when glitch mitigation is essential, aiding efficient and robust gravitational-wave inference in large data sets.

Abstract

Non-Gaussian noise in gravitational-wave detectors, known as "glitches," can bias the inferred parameters of transient signals when they occur nearby in time and frequency. These biases are addressed with a variety of methods that remove or otherwise mitigate the impact of the glitch. Given the computational cost and human effort required for glitch mitigation, we study the conditions under which it is strictly necessary. We consider simulated glitches and gravitational-wave signals in various configurations that probe their proximity both in time and in frequency. We determine that glitches located outside the time-frequency space spanned by the gravitational-wave model prior and with a signal-to-noise ratio, conservatively, below 50 do not impact estimation of the signal parameters.

Figures (7)

• Figure 1: Time-frequency breakdown of the CBC analysis window, schematically describing the ways in which glitches can be positioned with respect to signals. Region i@ (solid lines) encloses all time-frequency tracks (including those of higher order modes) within the analysis prior. For reference, in orange we plot the time-frequency content of the (2, 2) mode prior. Region ii@ contains glitches coincident in time and frequency, but never concurrently. We split between Region ii@a, those above the (4,4) mode in frequency, and Region ii@b, those below the (2,1) mode. Region iii@ contains glitches coincident in frequency, but not in time. Region iv@ contains glitches not coincident in frequency.
• Figure 2: Percentile–Percentile (P-P) plots for various simulations, each drawn from the same CBC prior (Table \ref{['tab:CBCPrior']}) but varying in glitch content (Table \ref{['tab:glitchPrior']}). The titles of each plot specify the relative time between the glitch distribution and the CBC time distribution, with the leftmost plot representing data without glitches and subsequent plots showing glitches progressively closer to the CBC. Each plot comprises 400 simulations, with recovery performed using only a CBC model. Each plot includes 15 lines, one for each CBC parameter, displaying the cumulative distribution function of the percentiles of the true values within their marginal posteriors. Lines are color-coded in red (blue) to indicate whether the parameter failed, $p \leq 0.05$ (passed, $p > 0.05$) the P-P test. A failure rejects the null hypothesis: the percentiles of the true values are uniformly distributed across their posteriors. Three-sigma confidence intervals are plotted in gray. Left: P–P plot when only the CBC model is simulated and recovered. All parameters pass the P-P test which serves as a baseline for the test and its implementation. Center (left to right): P–P plots for glitches in Region iii@. Right: P–P plot for glitches in Region i@. When glitches overlap with the signals, all parameters fail the P-P test.
• Figure 3: Top: Time domain whitened waveforms for the CBC (magenta) and a glitch with increasing SNR from 5 to 5000 (various colors) 0.38 s after the signal, see Table \ref{['tab:CBCInjection']} and Table \ref{['tab:glitchInjectionVarySNR']} for details. Second down: Efficiency when reweighting from a posterior on data with no glitch to a posterior on data with a glitch as a function of the glitch SNR. Bottom 3: 1-dimensional posteriors for select CBC parameters as a function of the glitch SNR. True values are marked in magenta. The direct-sampled posterior on glitch-impacted data is colored and the corresponding reweighted posterior is marked with black dashed lines. To the right we show the control dataset in gray, a posterior recovered from data with identical Gaussian noise but without any glitch; all gray posteriors are identical. For glitch SNR $\geq 1000$ we omit the direct-sampled posteriors due to nonphysical waveform behavior, further discussed in App. \ref{['app:waveform_conditioning']}. The efficiency, indicates that glitches louder than SNR 500 start impacting inference.
• Figure 4: Left: Time-frequency locations of 200 glitches simulated in Region iii@; after the signal. The glitch time-frequency locations are colored by the SNR at which that glitch induces a measurable change in the CBC posterior and are gray if the requisite SNR is above 500. For reference, Region i@ (black-dashed) and the time-frequency content of the (2,2) mode across the CBC prior (blue dots) are displayed. Bottom right: Maximum Jensen-Shannon divergence across CBC parameters between a CBC posterior on glitch-free data and the posterior on glitch-impacted data, plotted as a function of glitch SNR. Each curve corresponds to a glitch in the left figure. The black horizontal line is the threshold for posteriors considered distinct LIGOScientific:2020ibl. The gray dashed line is the JS divergence due only to stochastic sampling uncertainty Romero-Shaw:2020owr, plotted for reference. As the glitch SNR increases, so does does $D_\mathrm{JS}$. Top right: Cumulative distribution function of the number of glitches that induce a requisite divergence as a function of glitch SNR. Glitches below SNR 50 never induce a measurable bias. Higher-SNR glitches might induce a bias if within $0.5\,$s from the signal merger.
• Figure 5: Inference results on a GW150914-like signal with SNR 25 glitches of increasing frequency. Left: Time-frequency track of the GW150914-like signal with glitches overlaid. Region i@ is outlined in black-dashed lines; it includes the time-frequency content of the entire CBC model prior. The solid magenta line shows the (2,2) frequency track of the simulated CBC and the dotted lines display the higher-order modes, each labeled with its corresponding color. The location in time-frequency space of the glitches is shown in a colored box, each displaying three e-folds of the exponential time and frequency glitch decay. The only glitches that share time-frequency content with the GW model are the ones at 25 Hz and 50 Hz. Center: 1-dimensional posteriors for select CBC parameters from data with a glitch at the corresponding frequency (y-axis and color). In the lower half of each violin plot we show the posterior from identical data but without a glitch (gray); all such posteriors are identical. On the top of each violin plot are the posteriors recovered in glitch-impacted data; the colored posterior are those recovered with $\texttt{BayesWaveCpp}$ (direct sampling), and the black-dashed lines are the posterior obtained via reweighting. Right: Reweighting efficiency as a function of glitch frequency. Where the posteriors differ the most, at glitch frequency 25 Hz, the efficiency drops to 0. At 50 Hz, even though the posteriors are visually similar, the efficiency also dips to 50%, meaning that the glitch is nonetheless impacting the posterior.