Gravity Spy: Lessons Learned and a Path Forward

Abstract

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.

33 pages, 5 figures, published in European Physical Journal Plus for focus issue on "Citizen science for physics: From Education and Outreach to Crowdsourcing fundamental research"

References in corpus (61)

• Observation of Gravitational Waves from a Binary Black Hole Merger
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Advanced LIGO
• GW170104: Observation of a 50-Solar-Mass Binary Black Hole Coalescence at Redshift 0.2
• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• GW150914: The Advanced LIGO Detectors in the Era of First Discoveries
• Galaxy Zoo Green Peas: Discovery of A Class of Compact Extremely Star-Forming Galaxies
• KAGRA: 2.5 Generation Interferometric Gravitational Wave Detector
• The Sensitivity of the Advanced LIGO Detectors at the Beginning of Gravitational Wave Astronomy
• Galaxy Zoo: Exploring the Motivations of Citizen Science Volunteers
• Sensitivity and Performance of the Advanced LIGO Detectors in the Third Observing Run
• Data Mining and Machine Learning in Astronomy
• A guide to LIGO-Virgo detector noise and extraction of transient gravitational-wave signals
• Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Multiresolution techniques for the detection of gravitational-wave bursts
• LIGO Detector Characterization in the Second and Third Observing Runs
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
• Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
• Blip glitches in Advanced LIGO data
• Reducing Scattered Light in LIGO's Third Observing Run
• The LSC Glitch Group : Monitoring Noise Transients during the fifth LIGO Science Run
• Omicron: a tool to characterize transient noise in gravitational-wave detectors
• Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
• Environmental Noise in Advanced LIGO Detectors
• Finding the origin of noise transients in LIGO data with machine learning
• Characterising Transient Noise in the LIGO Detectors
• Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications
• Planet Hunters TESS II: Findings from the first two years of TESS
• Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning
• Application of dictionary learning to denoise LIGO's blip noise transients
• Impact of noise transients on low latency gravitational-wave event localisation
• Utilizing aLIGO Glitch Classifications to Validate Gravitational-Wave Candidates
• Inference-optimized AI and high performance computing for gravitational wave detection at scale
• Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors
• Improving significance of binary black hole mergers in Advanced LIGO data using deep learning : Confirmation of GW151216
• 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
• Transient glitch mitigation in Advanced LIGO data with $\text{glitschen}$
• Gaussian Processes for Glitch-robust Gravitational-wave Astronomy
• A semi-supervised Machine Learning search for never-seen Gravitational-Wave sources
• Daily monitoring of scattered light noise due to microseismic variability at the Virgo interferometer
• Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectors
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams
• 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
• On Improving the Performance of Glitch Classification for Gravitational Wave Detection by using Generative Adversarial Networks
• 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
• ArchEnemy: Removing scattered-light glitches from gravitational wave data
• Neural network time-series classifiers for gravitational-wave searches in single-detector periods
• How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series
• Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
• Noise in the LIGO Livingston Gravitational Wave Observatory due to Trains
• Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset
• 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
• Deep Learning for estimating parameters of Gravitational Waves
• DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics
• Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning

Cited by in corpus (14)

• LIGO Detector Characterization in the first half of the fourth Observing run
• New Gravitational Wave Discoveries Enabled by Machine Learning
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Assessing and Mitigating the Impact of Glitches on Gravitational-Wave Parameter Estimation: a Model Agnostic Approach
• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• No Glitch in the Matrix: Robust Reconstruction of Gravitational Wave Signals Under Noise Artifacts
• Search for gravitational waves from eccentric binary black holes with an effective-one-body template
• Classification uncertainty for transient gravitational-wave noise artefacts with optimised conformal prediction
• Participatory Science and Machine Learning Applied to Millions of Sources in the Hobby-Eberly Telescope Dark Energy Experiment
• Joint inference for gravitational wave signals and glitches using a data-informed glitch model
• Using t-SNE for characterizing glitches in LIGO detectors
• Multivariate Time Series Clustering for Environmental State Characterization of Ground-Based Gravitational-Wave Detectors
• 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