Identify real gravitational wave events in the LIGO-Virgo catalog GWTC-1 and GWTC-2 with convolutional neural network

Abstract

In recent years, machine learning models have been introduced into the field of gravitational wave (GW) data processing. In this paper, we apply the convolutional neural network (CNN) to LIGO O1, O2, O3a data analysis to search the released 41 GW events which are emitted from binary black hole (BBH) mergers (here we exclude the events from binary neutron star (BNS) mergers, and the events that are not detected simultaneously by Hanford (H) and Livingston (L) detectors), and use time sliding method to reduce the false alarm rate (FAR). According to the results, the 41 confirmed GW events of BBH mergers can be classified successfully by our CNN model. Furthermore, through restricting the number of consecutive prewarning from sequential samples intercepted continuously in LIGO O2 real time-series and vetoing the coincidences of noise from H and L, the FAR is limited to be less than once in 2 months. It is helpful to promote LIGO real time data processing.

Graphical abstract

Keywords

convolutional neural network / gravitational wave events / false alarm rate

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