2021MNRAS.508.1358S

In recent years, improvements in deep learning (DL) techniques towards gravitational wave (GW) astronomy have led to a significant rise in the development of various classification algorithms that have been successfully employed to extract GWs of binary black hole merger events from noisy time-series data. However, the success of these models is constrained by the length of time sample and the class of GW source: black hole binaries and neutron star binaries to some extent. In this work, we intended to advance the boundaries of DL techniques using convolutional neural networks, to go beyond binary classification and predict the physical parameters of the events. We aim to propose an alternative method that can be employed for real-time detection and parameter prediction. The DL model we present has been trained on 12s of data to predict the GW source parameters if detected. During training, the maximum accuracy attained was 90.93 per cent, with a validation accuracy of 89.97 per cent.