  The detection of gravitational waves is considered to be one of the most
magnificent discoveries of the century. Due to the high computational cost of
matched filtering pipeline, there is a hunt for an alternative powerful system.
I present, for the first time, the use of 1D residual neural network for
detection of gravitational waves. Residual networks have transformed many
fields like image classification, face recognition and object detection with
their robust structure. With increase in sensitivity of LIGO detectors we
expect many more sources of gravitational waves in the universe to be detected.
However, deep learning networks are trained only once. When used for
classification task, deep neural networks are trained to predict only a fixed
number of classes. Therefore, when a new type of gravitational wave is to be
detected, this turns out to be a drawback of deep learning. Shallow neural
networks can be used to learn data with simple patterns but fail to give good
results with increase in complexity of data. Remodelling the neural network
with detection of each new type of GW is highly infeasible. In this letter, I
also discuss ways to reduce the time required to adapt to such changes in
detection of gravitational waves for deep learning methods. Primarily, I aim to
create a custom residual neural network for 1-dimensional time series inputs,
which can learn a ton of features from dataset without giving up on increasing
the number of classes or increasing the complexity of data. I use the two class
of binary coalescence signals (Binary Black Hole Merger and Binary Neutron Star
Merger signals) detected by LIGO to check the performance of residual structure
on gravitational waves detection.
