Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

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.

27 pages, 8 figures, 1 table

References in corpus (25)

• Observation of Gravitational Waves from a Binary Black Hole Merger
• ADADELTA: An Adaptive Learning Rate Method
• GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence
• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• Characterization of the LIGO detectors during their sixth science run
• Binary Black Hole Mergers in the first Advanced LIGO Observing Run
• Properties of the Binary Black Hole Merger GW150914
• Detector configuration of KAGRA - the Japanese cryogenic gravitational-wave detector
• GW150914: The Advanced LIGO Detectors in the Era of First Discoveries
• FINDCHIRP: an algorithm for detection of gravitational waves from inspiraling compact binaries
• A chi-squared time-frequency discriminator for gravitational wave detection
• Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914
• Multiresolution techniques for the detection of gravitational-wave bursts
• Seismic isolation of Advanced LIGO: Review of strategy, instrumentation and performance
• The Milky Way Project: A statistical study of massive star formation associated with infrared bubbles
• A hierarchical method for vetoing noise transients in gravitational-wave detectors
• Classification methods for noise transients in advanced gravitational-wave detectors
• Galaxy Zoo: the effect of bar-driven fueling on the presence of an active galactic nucleus in disc galaxies
• Improving the Data Quality of Advanced LIGO Based on Early Engineering Run Results
• Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data
• The NINJA-2 project: Detecting and characterizing gravitational waveforms modelled using numerical binary black hole simulations
• The Red Radio Ring: a gravitationally lensed hyperluminous infrared radio galaxy at z=2.553 discovered through citizen science
• Neural Network Aided Glitch-Burst Discrimination and Glitch Classification
• Optimizing Vetoes for Gravitational-wave Transient Searches
• Instrumental vetoes for transient gravitational-wave triggers using noise-coupling models: The bilinear-coupling veto

Cited by in corpus (168)

• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
• GW170608: Observation of a 19-solar-mass Binary Black Hole Coalescence
• Sensitivity and Performance of the Advanced LIGO Detectors in the Third Observing Run
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Matching matched filtering with deep networks in gravitational-wave astronomy
• LIGO Detector Characterization in the Second and Third Observing Runs
• Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
• A New Search Pipeline for Compact Binary Mergers: Results for Binary Black Holes in the First Observing Run of Advanced LIGO
• Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• New binary black hole mergers in the LIGO--Virgo O3a data
• Enhancing Gravitational-Wave Science with Machine Learning
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Blip glitches in Advanced LIGO data
• Reducing Scattered Light in LIGO's Third Observing Run
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• Omicron: a tool to characterize transient noise in gravitational-wave detectors
• The Science of the Einstein Telescope
• Image-based deep learning for classification of noise transients in gravitational wave detectors
• Deep-Learning continuous gravitational waves
• Gravitational wave signal recognition of O1 data by deep learning
• Radio Galaxy Zoo: Compact and extended radio source classification with deep learning
• Noise Reduction in Gravitational-wave Data via Deep Learning
• All-sky search for short gravitational-wave bursts in the third Advanced LIGO and Advanced Virgo run
• AI-enhanced Collective Intelligence
• Noise spectral estimation methods and their impact on gravitational wave measurement of compact binary mergers
• Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
• Applications and Techniques for Fast Machine Learning in Science
• Transient Classification in LIGO data using Difference Boosting Neural Network
• Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
• Environmental Noise in Advanced LIGO Detectors
• Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
• Parameter Estimation and Model Selection of Gravitational Wave Signals Contaminated by Transient Detector Noise Glitches
• Characterising Transient Noise in the LIGO Detectors
• LIGO Detector Characterization in the first half of the fourth Observing run
• Efficient Gravitational-wave Glitch Identification from Environmental Data Through Machine Learning
• Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications
• Possible Causes of False General Relativity Violations in Gravitational Wave Observations
• YSE-PZ: A Transient Survey Management Platform that Empowers the Human-in-the-Loop
• A transient search using combined human and machine classifications
• Deep-Learning Continuous Gravitational Waves: Multiple detectors and realistic noise
• Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning
• Quantifying the Effect of Power Spectral Density Uncertainty on Gravitational-Wave Parameter Estimation for Compact Binary Sources
• Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network
• Accurate modeling and mitigation of overlapping signals and glitches in gravitational-wave data
• Effective Image Differencing with ConvNets for Real-time Transient Hunting
• Application of dictionary learning to denoise LIGO's blip noise transients
• Detection of Gravitational Waves Using Bayesian Neural Networks
• Gravitational-Wave Detector Networks: Standard Sirens on Cosmology and Modified Gravity Theory
• Extraction of Binary Black Hole Gravitational Wave Signals from Detector Data Using Deep Learning
• Impact of noise transients on low latency gravitational-wave event localisation
• Utilizing aLIGO Glitch Classifications to Validate Gravitational-Wave Candidates
• From One to Many: A Deep Learning Coincident Gravitational-Wave Search
• Space-based gravitational wave signal detection and extraction with deep neural network
• Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events
• Mock data study for next-generation ground-based detectors: The performance loss of matched filtering due to correlated confusion noise
• Optimization of model independent gravitational wave search using machine learning
• Inference-optimized AI and high performance computing for gravitational wave detection at scale
• Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning
• LSTM and CNN application for core-collapse supernova search in gravitational wave real data
• Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques
• Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors
• Investigation of the effects of non-Gaussian noise transients and their mitigation in parameterized gravitational-wave tests of general relativity
• Improving significance of binary black hole mergers in Advanced LIGO data using deep learning : Confirmation of GW151216
• Gravity Spy: Lessons Learned and a Path Forward
• Transient glitch mitigation in Advanced LIGO data with $\text{glitschen}$
• GWSkyNet: a real-time classifier for public gravitational-wave candidates
• An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
• Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks
• The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals
• Classification of gravitational-wave glitches via dictionary learning
• Gaussian Processes for Glitch-robust Gravitational-wave Astronomy
• A semi-supervised Machine Learning search for never-seen Gravitational-Wave sources
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectors
• Exploring gravitational-wave detection and parameter inference using Deep Learning methods
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams
• Daily monitoring of scattered light noise due to microseismic variability at the Virgo interferometer
• Generating transient noise artifacts in gravitational-wave detector data with generative adversarial networks
• GWitchHunters: Machine Learning and citizen science to improve the performance of Gravitational Wave detector
• Does non-stationary noise in LIGO and Virgo affect the estimation of $H_0$?
• Assessing and Mitigating the Impact of Glitches on Gravitational-Wave Parameter Estimation: a Model Agnostic Approach
• Generalised gravitational burst generation with Generative Adversarial Networks
• Real-Time Detection of Unmodelled Gravitational-Wave Transients Using Convolutional Neural Networks
• On Improving the Performance of Glitch Classification for Gravitational Wave Detection by using Generative Adversarial Networks
• Glitch subtraction from gravitational wave data using adaptive spline fitting
• Convolutional neural network for gravitational-wave early alert: Going down in frequency
• Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources
• Identifying glitches near gravitational-wave signals from compact binary coalescences using the Q-transform
• SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients
• GWSkyNet-Multi: A Machine Learning Multi-Class Classifier for LIGO-Virgo Public Alerts
• Enhancing the significance of gravitational wave bursts through signal classification
• Application of the independent component analysis to the iKAGRA data
• Ranking Candidate Signals with Machine Learning in Low-Latency Search for Gravitational-Waves from Compact Binary Mergers
• Machine learning phases and criticalities without using real data for training
• Bursts from Space: MeerKAT - The first citizen science project dedicated to commensal radio transients
• Bilinear noise subtraction at the GEO 600 observatory
• ArchEnemy: Removing scattered-light glitches from gravitational wave data
• Long-duration transient, gravitational-wave search pipeline
• New methods to assess and improve LIGO detector duty cycle
• Gwadaptive_scattering: an automated pipeline for scattered light noise characterization
• Neural network time-series classifiers for gravitational-wave searches in single-detector periods
• Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
• Localization of gravitational waves using machine learning
• Interpretable Faraday Complexity Classification
• How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series
• Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
• Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset
• Noise in the LIGO Livingston Gravitational Wave Observatory due to Trains
• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• A Coincidence Null Test for Poisson-Distributed Events
• Computational Techniques for Parameter Estimation of Gravitational Wave Signals
• Data-driven detection of multi-messenger transients
• A convolutional neural network to distinguish glitches from minute-long gravitational wave transients
• Inferring the Astrophysical Population of Gravitational Wave Sources in the Presence of Noise Transients
• Matched-filter study and energy budget suggest no detectable gravitational-wave 'extended emission' from GW170817
• Classifying binary black holes from Population III stars with the Einstein Telescope: A machine-learning approach
• Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1
• Optimizing the Human-Machine Partnership with Zooniverse
• Deep Learning for estimating parameters of Gravitational Waves
• Reconstruction of binary black hole harmonics in LIGO using deep learning
• cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation
• No Glitch in the Matrix: Robust Reconstruction of Gravitational Wave Signals Under Noise Artifacts
• CzSL: Learning from citizen science, experts and unlabelled data in astronomical image classification
• Search for gravitational waves from eccentric binary black holes with an effective-one-body template
• Galaxy Zoo: Clump Scout -- Design and first application of a two-dimensional aggregation tool for citizen science
• Using machine learning to study the kinematics of cold gas in galaxies
• Classification uncertainty for transient gravitational-wave noise artefacts with optimised conformal prediction
• DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning
• Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories
• UniMAP: Model-free detection of unclassified noise transients in LIGO-Virgo data using the Temporal Outlier Factor
• DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics
• PAH Marks the Spot: Digging for Buried Clusters in Nearby Star-forming Galaxies
• Joint inference for gravitational wave signals and glitches using a data-informed glitch model
• All-sky search for long-duration gravitational-wave bursts in the third Advanced LIGO and Advanced Virgo run
• Ultra-Faint Milky Way Satellites Discovered in Carina, Phoenix, and Telescopium with DELVE Data Release 3
• Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning
• Sensing and vetoing loud transient noises for the gravitational-wave detection
• Real-time regression analysis with deep convolutional neural networks
• Application of a new transient-noise analysis tool for an unmodeled gravitational-wave search pipeline
• Improved binary black hole searches through better discrimination against noise transients
• Impact of noise transients on gravitational-wave burst detection efficiency of the BayesWave pipeline with multi-detector networks
• Searching for topological dark matter in LIGO data
• Deep Multi-view Models for Glitch Classification
• Unmasking noise transients masquerading as intermediate-mass black hole binaries
• Chirp mass based glitch identification in long duration gravitational wave transients
• Using t-SNE for characterizing glitches in LIGO detectors
• Multi-detector null-stream-based ${\chi }^2$ statistic for compact binary coalescence searches
• A Gaussian process framework for testing general relativity with gravitational waves
• Improved Binary Black Hole Search Discriminator from the Singular Value Decomposition of Non-Gaussian Noise Transients
• Learning to detect continuous gravitational waves: an open data-analysis competition
• $\beta$-Annealed Variational Autoencoder for glitches
• Glitch veto based on unphysical gravitational wave binary inspiral templates
• The Hubble Image Similarity Project
• Machine Learning Confirms GW231123 is a "Lite" Intermediate Mass Black Hole Merger
• Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO, Advanced Virgo and KAGRA
• Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets
• Identification of Noise-Associated Glitches in KAGRA O3GK with Hveto
• MaxWave: Rapid maximum likelihood wavelet reconstruction of non-Gaussian features in gravitational wave data
• Hierarchical search for compact binary coalescences in the Advanced LIGO's first two observing runs
• Unveiling gravitational waves from core-collapse supernovae with MUSE
• A machine learning algorithm for minute-long Burst searches
• Extract non-Gaussian Features in Gravitational Wave Observation Data Using Self-Supervised Learning
• Deep Learning Techniques to make Gravitational Wave Detections from Weak Time-Series Data