SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients

Abstract

Blip glitches, a type of short-duration noise transient in the LIGO--Virgo data, are a nuisance for the binary black hole (BBH) searches. They affect the BBH search sensitivity significantly because their time-domain morphologies are very similar, and that creates difficulty in vetoing them. In this work, we construct a deep-learning neural network to efficiently distinguish BBH signals from blip glitches. We introduce sine-Gaussian projection (SGP) maps, which are projections of GW frequency-domain data snippets on a basis of sine-Gaussians defined by the quality factor and central frequency. We feed the SGP maps to our deep-learning neural network, which classifies the BBH signals and blips. Whereas the BBH signals are simulated, the blips used are taken from real data throughout our analysis. We show that our network significantly improves the identification of the BBH signals in comparison to the results obtained using traditional-${\chi }^2$ and sine-Gaussian ${\chi }^2$. For example, our network improves the sensitivity by 75% at a false-positive rate of $10^{-2}$ for BBHs with total mass in the range $[80,140]M_{\odot }$ and SNR in the range $[3,8]$. Also, it correctly identifies 95% of the real GW events in GWTC-3. The computation time for classification is a few minutes for thousands of SGP maps on a single core. With further optimisation in the next version of our algorithm, we expect a further reduction in the computational cost. Our proposed method can potentially improve the veto process in the LIGO--Virgo GW data analysis and conceivably support identifying GW signals in low-latency pipelines.

arXiv comment11 pages, 8 figures and 2 tables. Reviewed by LIGO Scientific Collaboration (LSC) with preprint number LIGO-P2100485

References in corpus (42)

• Deep Learning in Neural Networks: An Overview
• Observation of Gravitational Waves from a Binary Black Hole Merger
• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
• Advanced LIGO
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• A simple model of complete precessing black-hole-binary gravitational waveforms
• The PyCBC search for gravitational waves from compact binary coalescence
• FINDCHIRP: an algorithm for detection of gravitational waves from inspiraling compact binaries
• Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo
• KAGRA: 2.5 Generation Interferometric Gravitational Wave Detector
• A guide to LIGO-Virgo detector noise and extraction of transient gravitational-wave signals
• A chi-squared time-frequency discriminator for gravitational wave detection
• Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914
• New binary black hole mergers in the second observing run of Advanced LIGO and Advanced Virgo
• Higher-order spin effects in the amplitude and phase of gravitational waveforms emitted by inspiraling compact binaries: Ready-to-use gravitational waveforms
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Matching matched filtering with deep networks in gravitational-wave astronomy
• 2-OGC: Open Gravitational-wave Catalog of binary mergers from analysis of public Advanced LIGO and Virgo data
• LIGO Detector Characterization in the Second and Third Observing Runs
• Detecting binary compact-object mergers with gravitational waves: Understanding and Improving the sensitivity of the PyCBC search
• PyCBC Live: Rapid Detection of Gravitational Waves from Compact Binary Mergers
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Effects of Data Quality Vetoes on a Search for Compact Binary Coalescences in Advanced LIGO's First Observing Run
• Blip glitches in Advanced LIGO data
• The Science Case for LIGO-India
• Extending the PyCBC search for gravitational waves from compact binary mergers to a global network
• Omicron: a tool to characterize transient noise in gravitational-wave detectors
• Distinguishing short duration noise transients in LIGO data to improve the PyCBC search for gravitational waves from high mass binary black hole mergers
• Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
• Transient Classification in LIGO data using Difference Boosting Neural Network
• Detection of gravitational-wave signals from binary neutron star mergers using machine learning
• Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning
• Utilizing aLIGO Glitch Classifications to Validate Gravitational-Wave Candidates
• Effect of sine-Gaussian glitches on searches for binary coalescence
• Generalized Approach to Matched Filtering using Neural Networks
• Improving significance of binary black hole mergers in Advanced LIGO data using deep learning : Confirmation of GW151216
• Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
• A classifier for gravitational-wave inspiral signals in non-ideal single-detector data
• Towards mitigating the effect of sine-Gaussian noise transients on searches for gravitational waves from compact binary coalescences
• A unified approach to ${\chi }^2$ discriminators for searches of gravitational waves from compact binary coalescences
• An optimal ${\chi }^2$ discriminator against modelled noise-transients in interferometric data in searches for binary black-hole mergers

Cited by in corpus (9)

• Deep Residual Networks for Gravitational Wave Detection
• Gravity Spy: Lessons Learned and a Path Forward
• New Gravitational Wave Discoveries Enabled by Machine Learning
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Premerger detection of massive black hole binaries using deep learning
• SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data
• Neural network time-series classifiers for gravitational-wave searches in single-detector periods
• Novel Deep Learning Approach to Detecting Binary Black Hole Mergers
• Improved binary black hole searches through better discrimination against noise transients