2021
DOI: 10.1103/physrevd.103.044013
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Modeling compact binary signals and instrumental glitches in gravitational wave data

Abstract: Transient non-Gaussian noise in gravitational wave detectors, commonly referred to as glitches, pose challenges for detection and inference of the astrophysical properties of detected signals when the two are coincident in time. Current analyses aim toward modeling and subtracting the glitches from the data using a flexible, morphology-independent model in terms of sine-Gaussian wavelets before the signal source properties are inferred using templates for the compact binary signal. We present a new analysis of… Show more

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Cited by 52 publications
(64 citation statements)
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“…Due to the correlations between masses and spins [138][139][140], multimodality in mass distributions may also translate to multiple peaks in the effective inspiral spin distribution. Multimodality can arise due to: the complexity of the likelihood surface when using waveform models that include higher-order multipole moments [19,[141][142][143] and precession [144,145], noise fluctuations for quiet signals [146], the presence of glitches [147][148][149], or there being multiple overlapping signals in the data (which is unlikely given O3 sensitivity) [150]. Therefore, multimodality is expected in a few cases.…”
Section: Source Propertiesmentioning
confidence: 99%
“…Due to the correlations between masses and spins [138][139][140], multimodality in mass distributions may also translate to multiple peaks in the effective inspiral spin distribution. Multimodality can arise due to: the complexity of the likelihood surface when using waveform models that include higher-order multipole moments [19,[141][142][143] and precession [144,145], noise fluctuations for quiet signals [146], the presence of glitches [147][148][149], or there being multiple overlapping signals in the data (which is unlikely given O3 sensitivity) [150]. Therefore, multimodality is expected in a few cases.…”
Section: Source Propertiesmentioning
confidence: 99%
“…A more restricted signal model would then assume that the tensor modes are given by regular CBC templates with some parametrized deviation from GR (such as the parametrized post-Einsteinian framework [32]), while the nontensor modes are again modeled with sine-Gaussian wavelets. The possibility of joint analyses using CBC templates and wavelets with BayesWave was recently demonstrated in [86], so we plan to explore this option in future work.…”
Section: Discussionmentioning
confidence: 99%
“…But some of them are contaminated by glitches which may cause poor convergence and bias in parameter estimation as discussed in (Powell 2018;Chatziioannou et al 2021). The reconstruction and subtraction of glitches have been performed by LVK with the BayesWave algorithm (Littenberg et al 2016;.…”
Section: Appendixmentioning
confidence: 99%