Transient Classification in LIGO data using Difference Boosting Neural Network

Abstract

Detection and classification of transients in data from gravitational wave detectors are crucial for efficient searches for true astrophysical events and identification of noise sources. We present a hybrid method for classification of short duration transients seen in gravitational wave data using both supervised and unsupervised machine learning techniques. To train the classifiers we use the relative wavelet energy and the corresponding entropy obtained by applying one-dimensional wavelet decomposition on the data. The prediction accuracy of the trained classifier on 9 simulated classes of gravitational wave transients and also LIGO's sixth science run hardware injections are reported. Targeted searches for a couple of known classes of non-astrophysical signals in the first observational run of Advanced LIGO data are also presented. The ability to accurately identify transient classes using minimal training samples makes the proposed method a useful tool for LIGO detector characterization as well as searches for short duration gravitational wave signals.

9 pages, 6 figures; Expanded discussion on classifier, feature extraction and simulated data

References in corpus (4)

• GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Coherent method for detection of gravitational wave bursts
• Towards mitigating the effect of sine-Gaussian noise transients on searches for gravitational waves from compact binary coalescences

Cited by in corpus (51)

• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Matching matched filtering with deep networks in gravitational-wave astronomy
• Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• Enhancing Gravitational-Wave Science with Machine Learning
• Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
• Image-based deep learning for classification of noise transients in gravitational wave detectors
• Analysing the 21cm signal from the Epoch of Reionization with artificial neural networks
• Deep-Learning continuous gravitational waves
• Gravitational wave signal recognition of O1 data by deep learning
• Noise Reduction in Gravitational-wave Data via Deep Learning
• Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
• Learning physical properties of liquid crystals with deep convolutional neural networks
• Accuracy of Estimating Highly Eccentric Binary Black Hole Parameters with Gravitational-Wave Detections
• 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
• Deep-Learning Continuous Gravitational Waves: Multiple detectors and realistic noise
• Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning
• Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network
• Gravitational Wave observatories may be able to detect hyperbolic encounters of Black Holes
• Effective Image Differencing with ConvNets for Real-time Transient Hunting
• Application of dictionary learning to denoise LIGO's blip noise transients
• Determining liquid crystal properties with ordinal networks and machine learning
• Improved deep learning techniques in gravitational-wave data analysis
• Utilizing aLIGO Glitch Classifications to Validate Gravitational-Wave Candidates
• Generalized Approach to Matched Filtering using Neural Networks
• LSTM and CNN application for core-collapse supernova search in gravitational wave real data
• 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
• An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
• Classification of gravitational-wave glitches via dictionary learning
• Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams
• Enhancing the sensitivity of transient gravitational wave searches with Gaussian Mixture Models
• SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients
• Ranking Candidate Signals with Machine Learning in Low-Latency Search for Gravitational-Waves from Compact Binary Mergers
• New methods to assess and improve LIGO detector duty cycle
• Bilinear noise subtraction at the GEO 600 observatory
• Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
• Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset
• A machine learning approach for GRB detection in AstroSat CZTI data
• Novel Deep Learning Approach to Detecting Binary Black Hole Mergers
• Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories
• Real-time regression analysis with deep convolutional neural networks
• Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning
• Search for exotic gravitational wave signals beyond general relativity using deep learning
• Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks
• Multi-detector null-stream-based ${\chi }^2$ statistic for compact binary coalescence searches
• Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets
• Interpretable liquid crystal phase classification via two-by-two ordinal patterns