Searches for Compact Binary Coalescence Events using Neural Networks in LIGO/Virgo Second Observation Period

Abstract

We present results on the search for the coalescence of compact binary mergers using convolutional neural networks and the LIGO/Virgo data, corresponding to the O2 observation period. Two-dimensional images in time and frequency are used as input, and two sets of neural networks are trained separately for low mass (0.2 - 2.0 Msun) and high mass (25 - 100 Msun) compact binary coalescence events. We explored neural networks trained with input information from a single or a pair of interferometers, indicating that the use of information from pairs leads to an improved performance. A scan over the full O2 data set using the convolutional neural networks for detection demonstrates that the performance is compatible with that from canonical pipelines using matched filtering techniques. No additional events with significant signal-to-noise ratio are found in the O2 data.

arXiv comment6 pages, 6 figures, 4 tables

References in corpus (18)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Observation of Gravitational Waves from a Binary Black Hole Merger
• Deep Residual Learning for Image Recognition
• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
• The PyCBC search for gravitational waves from compact binary coalescence
• Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo
• A guide to LIGO-Virgo detector noise and extraction of transient gravitational-wave signals
• 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
• Phenomenological model for the gravitational-wave signal from precessing binary black holes with two-spin effects
• Enhancing Gravitational-Wave Science with Machine Learning
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data
• Image-based deep learning for classification of noise transients in gravitational wave detectors
• Finding the origin of noise transients in LIGO data with machine learning
• Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
• Ranking Candidate Signals with Machine Learning in Low-Latency Search for Gravitational-Waves from Compact Binary Mergers

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