Localization of gravitational waves using machine learning

An observation of gravitational waves is a trigger of the multimessenger search of an astronomical event. A combination of the data from two or three gravitational wave detectors indicates the location of a source and low-latency data analysis is key to transferring the information to other detectors sensitive at different wavelengths. In contrast to the current method, which relies on the matched-filtering technique, we proposed the use of machine learning that is much faster and possibly more accurate than matched filtering. Our machine-learning method is a combination of the method proposed by Chatterjee et al. and a method using the temporal convolutional network. We demonstrate the sky localization of a gravitational-wave source using four detectors; LIGO H1, LIGO L1, Virgo, and KAGRA, and compare the result in the case without KAGRA to examine the positive influence of having the fourth detector in the global gravitationalwave network.