Detection of Einstein telescope gravitational wave signals from binary black holes using deep learning

Abstract

ABSTRACT The expected volume of data from the third-generation gravitational waves (GWs) Einstein Telescope (ET) detector would make traditional GWs search methods such as match filtering impractical. This is due to the large template bank required and the difficulties in waveforms modelling. In contrast, machine learning (ML) algorithms have shown a promising alternative for GWs data analysis, where ML can be used in developing semi-automatic and automatic tools for the detection and parameter estimation of GWs sources. Compared to second generation detectors, ET will have a wider accessible frequency band but also a lower noise. The ET will have a detection rate for Binary Black Holes (BBHs) and Binary Neutron Stars (BNSs) of the order of 105–106 and 7 × 104 yr−1, respectively. We explored the efficiency of using convolutional neural networks (CNNs) for the detection of BBHs’ mergers in synthetic noisy data that was generated according to ET’s parameters. Without performing data whitening or applying bandpass filtering, we trained four CNN networks with the state-of-the-art performance in computer vision, namely VGG, ResNet, and DenseNet. ResNet has significantly better performance, and was able to detect BBHs sources with SNR of 8 or higher with 98.5 per cent accuracy, and with 92.5 per cent, 85 per cent, 60 per cent, and 62 per cent accuracy for sources with SNR range of 7–8, 6–7, 5–6, and 4–5, respectively. ResNet, in qualitative evaluation, was able to detect a BBH’s merger at 60 Gpc with 4.3 SNR. It was also shown that CNN can be used efficiently for near-real time detection of BBHs.