Ensemble of Deep Convolutional Neural Networks for real-time gravitational wave signal recognition

Abstract

With the rapid development of deep learning technology, more and more researchers apply it to gravitational wave (GW) data analysis. Previous studies focused on a single deep learning model. In this paper we design an ensemble algorithm combining a set of convolutional neural networks (CNN) for GW signal recognition. The whole ensemble model consists of two sub-ensemble models. Each sub-ensemble model is also an ensemble model of deep learning. The two sub-ensemble models treat data of Hanford and Livinston detectors respectively. Proper voting scheme is adopted to combine the two sub-ensemble models to form the whole ensemble model. We apply this ensemble model to all reported GW events in the first observation and second observation runs (O1/O2) by LIGO-VIRGO Scientific Collaboration. We find that the ensemble algorithm can clearly identify all binary black hole merger events except GW170818. We also apply the ensemble model to one month (August 2017) data of O2. There is no false trigger happens although only O1 data are used for training. Our test results indicate that the ensemble learning algorithms can be used in real-time GW data analysis.

The test code and the source data are available at https://github.com/WeiWang31/EnsembleModel

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Cited by in corpus (4)

• Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection
• Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
• Inferring the spin distribution of binary black holes using deep learning
• Denoising gravitational wave with deep learning in the time-frequency domain