A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
abstract
(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.
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representative citing papers
LIGO and Virgo detected 39 compact binary coalescence events in O3a, including 13 new ones, with black hole binaries up to 150 solar masses and the first significantly asymmetric mass ratios.
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
DQRbuild toolkit automates data quality vetting for gravitational-wave events, recovering 96% of human-identified issues from O3 with a 24% false alarm rate.
Reanalysis of flagged LVK events with waveform uncertainty models produces consistent spin and precession inferences across raw/deglitched data and multiple waveform approximants.
Volunteers propose new glitch categories in LIGO data that connect to instrument states and pose difficulties for existing ML glitch classifiers.
VIGILant applies tree-based models and a ResNet CNN to classify Virgo O3b glitches with 98% accuracy and has been deployed for daily use with an interactive dashboard.
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.
citing papers explorer
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Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection
A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
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GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
LIGO and Virgo detected 39 compact binary coalescence events in O3a, including 13 new ones, with black hole binaries up to 150 solar masses and the first significantly asymmetric mass ratios.
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Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit
DQRbuild toolkit automates data quality vetting for gravitational-wave events, recovering 96% of human-identified issues from O3 with a 24% false alarm rate.
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Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data
Reanalysis of flagged LVK events with waveform uncertainty models produces consistent spin and precession inferences across raw/deglitched data and multiple waveform approximants.
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Hunting for new glitches in LIGO data using community science
Volunteers propose new glitch categories in LIGO data that connect to instrument states and pose difficulties for existing ML glitch classifiers.
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VIGILant: an automatic classification pipeline for glitches in the Virgo detector
VIGILant applies tree-based models and a ResNet CNN to classify Virgo O3b glitches with 98% accuracy and has been deployed for daily use with an interactive dashboard.
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Inferring the properties of a population of compact binaries in presence of selection effects
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.