Gravitational wave signal recognition of O1 data by deep learning

TL;DR

This paper applies an adjusted deep learning convolutional neural network to LIGO O1 data, successfully recognizing confirmed gravitational wave events and identifying new triggers, demonstrating the effectiveness of deep learning in gravitational wave detection.

Contribution

The study adapts and improves CNN architecture for gravitational wave data, achieving comparable accuracy and uncovering new potential signals in O1 data.

Findings

Successfully recognized 11 confirmed events in O1 and O2 data.

Identified approximately 2000 new gravitational wave triggers.

Demonstrated the effectiveness of an adjusted CNN for weak signal detection.

Abstract

Deep learning method develops very fast as a tool for data analysis these years. Such a technique is quite promising to treat gravitational wave detection data. There are many works already in the literature which used deep learning technique to process simulated gravitational wave data. In this paper we apply deep learning to LIGO O1 data. In order to improve the weak signal recognition we adjust the convolutional neural network (CNN) a little bit. Our adjusted convolutional neural network admits comparable accuracy and efficiency of signal recognition as other deep learning works published in the literature. Based on our adjusted CNN, we can clearly recognize the eleven confirmed gravitational wave events included in O1 and O2. And more we find about 2000 gravitational wave triggers in O1 data.