Deep Learning for estimating parameters of Gravitational Waves
  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 blackhole 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: binary blackhole 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 realtime 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%, with a validation accuracy of 89.97%.
