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

TL;DR

This paper introduces an ensemble of deep convolutional neural networks for real-time gravitational wave detection, effectively identifying all major events in LIGO-VIRGO data without false triggers.

Contribution

It presents a novel ensemble approach combining multiple CNNs for gravitational wave recognition, enhancing detection accuracy and real-time capability.

Findings

Successfully identified all GW events in O1/O2 data except GW170818.

No false triggers occurred during one month of O2 data testing.

Ensemble method is suitable for real-time GW data analysis.

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.