Comparison of training methods for Convolutional Neural Network model for Gravitational-Wave detection from Neutron Star$-$Black Hole Binaries

Abstract

The traditional method of Gravitational Wave (GW) detection is Matched Filtering that was used for the first GW detection by aLIGO in 2015. The method works by matching the observation data sample with a set of templates of known GW waveforms. Iterating through all the templates for relatively complex GW signals, for instance those from eccentric sources, increases the overall computational cost and time complexity. In recent years, Machine Learning techniques have been probed as a solution to this problem. In this short paper