Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning

Colgan, R.; Márka, Z.; Yan, Jingkai; Bartos, I.; Wright, John; Márka, S.

doi:10.48550/arxiv.2203.05086

Cited by 5 publications

(5 citation statements)

References 39 publications

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“…In order to have an accurate and unbiased catalog of GW events, effective and generic methods for separating signal and glitch power are necessary. Proposed approaches for glitch subtraction include removing the contaminated data [23,[26][27][28][29][30] or subtracting detector noise based on data from auxiliary channels [13,[31][32][33][34][35][36][37][38][39]. The glitches discussed in this paper are those that remain after the noise mitigation described in [35,39].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…The proposed HDN can potentially have wider applications within the field of gravitational wave science. For example, in the task of glitch detection and identification [80,[99][100][101][102][103], one can combine existing constructions of machine learning based models with hierarchical models, to improve on both efficiency and accuracy.…”

Section: Discussionmentioning

confidence: 99%

Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks

Yan¹,

Colgan²,

Wright³

et al. 2022

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“…The proposed HDN can potentially have wider applications within the field of gravitational wave science. For example, in the task of glitch detection and identification [80,[100][101][102][103][104], one can combine existing constructions of machine learning based models with hierarchical models, to improve on both efficiency and accuracy.…”

Section: Discussionmentioning

confidence: 99%

Boosting the efficiency of parametric detection with hierarchical neural networks

et al. 2022

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Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning

Cited by 5 publications

References 39 publications

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

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

Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks

Boosting the efficiency of parametric detection with hierarchical neural networks

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