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Despite achieving sensitivities capable of detecting the extremely small amplitude of gravitational waves (GWs), LIGO and Virgo detector data contain frequent bursts of non-Gaussian transient noise, commonly known as 'glitches'.
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“Advanced LIGO”
LIGO Scientific Collaboration et al · 2015
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“Advanced Virgo: a second-generation interferometric gravitational wave detector”
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“An Introduction to Convolutional Neural Networks” arXiv:1511.08458 [cs]
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“Observation of Gravitational Waves from a Binary Black Hole Merger”
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“GW150914: The Advanced LIGO Detectors in the Era of First Discoveries”
B.. Abbott et al · 2016
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“Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914”
B Abbott et al · 2016
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Joseph. Areeda et al · 2016
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“Frequency-domain gravitational waves from nonprecessing black-hole binaries. I. New numerical waveforms and anatomy of the signal”
Sascha Husa et al · 2016
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“Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era”
Sebastian Khan et al · 2016
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“Calibration of the Advanced LIGO detectors for the discovery of the binary black-hole merger GW150914”
B.. Abbott et al · 2017
Cited alongside, same era.
“Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science”
M Zevin et al · 2017
Cited alongside, same era.
“Validating gravitational-wave detections: The Advanced LIGO hardware injection system”
C. Biwer et al · 2017
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“Classification methods for noise transients in advanced gravitational-wave detectors II: Performance tests on Advanced LIGO data”
Jade Powell et al · 2017
Cited alongside, same era.
“Glitch Classification and Clustering for LIGO with Deep Transfer Learning”
Daniel George, Hongyu Shen and Eliu Huerta · 2017
Cited alongside, same era.
“iDQ: Statistical inference of non-gaussian noise with auxiliary degrees of freedom in gravitational-wave detectors”
Reed Essick et al · 2020
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“GraceDB—Gravitational-Wave Candidate Event Database”, 2020
Pace A, Prestegard T, Moe B and Stephens B · 2020
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“GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run”
The LIGO Scientific Collaboration et al · 2021
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“LIGO detector characterization in the second and third observing runs”
D Davis et al · 2021
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“Discovering features in gravitational-wave data through detector characterization, citizen science, and machine learning”
S Soni et al · 2021
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“Effects of data quality vetoes on a search for compact binary coalescences in Advanced LIGO’s first observing run”
B.. Abbott et al · 2018
Cited alongside, same era.
“Parameter estimation and model selection of gravitational wave signals contaminated by transient detector noise glitches”
Jade Powell · 2018
Cited alongside, same era.
“Machine learning for Gravity Spy: Glitch classification and dataset”
S. Bahaadini et al · 2018
Cited alongside, same era.
“Data Quality Report user documentation”, 2018
The LIGO Scientific Collaboration and The Virgo Collaboration · 2018
Cited alongside, same era.
“How Does the Data set Affect CNN-based Image Classification Performance?”
Chao Luo et al · 2018
Cited alongside, same era.
“LIGO Algorithm Library - LALSuite”, free software (GPL), 2018
The LIGO Scientific Collaboration · 2018
Cited alongside, same era.
“Improving astrophysical parameter estimation via offline noise subtraction for Advanced LIGO”
J.. Driggers et al · 2019
Cited alongside, same era.
LIGO Scientific Collaboration, Virgo Collaboration and KAGRA Collaboration · 2021
Later among the works it cites.
“Subtracting glitches from gravitational-wave detector data during the third LIGO-Virgo observing run”
D Davis et al · 2022
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“Impact of noise transients on low latency gravitational-wave event localization”
Ronaldas Macas et al · 2022
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“Curious case of GW200129: Interplay between spin-precession inference and data-quality issues”
Ethan Payne et al · 2022
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“Review of the Advanced LIGO Gravitational Wave Observatories Leading to Observing Run Four”
Craig Cahillane and Georgia Mansell · 2022
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“Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow”
Aurélien Géron · 2022
Later among the works it cites.
“Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications”
J Glanzer et al · 2023
Closest in time.
“ GWSkyNet
Miriam Cabero, Ashish Mahabal and Jess McIver · 2041
Closest in time.