Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks

Yu, Hang; Adhikari, R. X.

doi:10.3389/frai.2022.811563

Cited by 17 publications

(8 citation statements)

References 74 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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“…( 5) by √ 2. To tackle the nonstationarities in the interferometer, one could utilize auxiliary channels in LIGO [78,79]. Furthermore, with auxiliary channels one could predict not only the expected spectrum of the quantum shot noise P a but also other noise sources across the entire spectra.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Exposing Gravitational Waves below the Quantum Shot Noise

Yu,

Martynov,

Adhikari

et al. 2022

Preprint

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The sensitivities of ground-based gravitational wave (GW) detectors are limited by quantum shot noise at a few hundred Hertz and above. Nonetheless, one can use a quantum-correlation technique proposed by Martynov, et al. [Phys. Rev. A 95, 043831 (2017)] to remove the expectation value of the shot noise, thereby exposing underlying classical signals in the cross spectrum formed by cross-correlating the two outputs in a GW interferometer's anti-symmetric port. We explore here the prospects and analyze the sensitivity of using quantum correlation to detect astrophysical GW signals. Conceptually, this technique is similar to the correlation of two different GW detectors as it utilizes the fact that a GW signal will be correlated in the two outputs but the shot noise will be uncorrelated. Quantum correlation also has its unique advantages as it requires only a single interferometer to make a detection. Therefore, quantum correlation could increase the duty cycle, enhance the search efficiency, and enable the detection of highly polarized signals. In particular, we show that quantum correlation could be especially useful for detecting post-merger remnants of binary neutron stars with both short (< 1 s) and intermediate (∼ 10 − 10 4 s) durations and setting upper limits on continuous emissions from unknown pulsars.

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“…Weiner filtering is particularly useful in cases where a linear coupling exists between the noise source and the gravitational-wave strain data. Additional subtraction procedures based on machine learning [127][128][129] are also being explored. These new methods have shown promise in subtracting sources of noise that exhibit non-linear couplings to the gravitational-wave strain.…”

Section: Noise Subtractionmentioning

confidence: 99%

Detector Characterization and Mitigation of Noise in Ground-Based Gravitational-Wave Interferometers

Davis

Walker

2022

Galaxies

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Since the early stages of operation of ground-based gravitational-wave interferometers, careful monitoring of these detectors has been an important component of their successful operation and observations. Characterization of gravitational-wave detectors blends computational and instrumental methods of investigating the detector performance. These efforts focus both on identifying ways to improve detector sensitivity for future observations and understand the non-idealized features in data that has already been recorded. Alongside a focus on the detectors themselves, detector characterization includes careful studies of how astrophysical analyses are affected by different data quality issues. This article presents an overview of the multifaceted aspects of the characterization of interferometric gravitational-wave detectors, including investigations of instrumental performance, characterization of interferometer data quality, and the identification and mitigation of data quality issues that impact analysis of gravitational-wave events. Looking forward, we discuss efforts to adapt current detector characterization methods to meet the changing needs of gravitational-wave astronomy.

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Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks

Cited by 17 publications

References 74 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

Exposing Gravitational Waves below the Quantum Shot Noise

Detector Characterization and Mitigation of Noise in Ground-Based Gravitational-Wave Interferometers

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