Gravitational-wave signal recognition of LIGO data by deep learning
He Wang, Shichao Wu, Zhoujian Cao, Xiaolin Liu, Jian-Yang Zhu
Abstract
The deep learning method has developed very fast as a tool for data analysis in recent years. Moreover, as a technique, it is quite promising as a way to analyze gravitational-wave detection data. Multiple works in the literature have already used deep learning to process simulated gravitational-wave data. In this paper, we apply deep learning to LIGO data. In order to improve the weak signal recognition, we design a new structure of the convolutional neural network (CNN). The key feature of our new CNN structure is the sensing layer. This layer mimics matched filtering but is different from the usual matched-filtering technique. Usually, the matched-filtering technique uses a full template bank to match the data. However, our sensing layer only uses tens of waveforms. Our new convolutional neural network admits comparable accuracy and efficiency of signal recognition compared to other deep learning works published in the literature. Based on our new CNN, we can clearly recognize the 11 confirmed gravitational-wave events included in O1 and O2. In addition, we find about 2000 gravitational-wave triggers in O1 data.