Detecting residues of cosmic events using residual neural network

TL;DR

This paper introduces a 1D residual neural network approach for gravitational wave detection, aiming to improve adaptability and reduce retraining time compared to traditional deep learning methods.

Contribution

It presents the first use of 1D residual neural networks for gravitational wave detection, enhancing feature learning and adaptability to new classes of signals.

Findings

Effective detection of binary black hole and neutron star merger signals

Reduced retraining time for new gravitational wave classes

Improved feature extraction from time series data

Abstract

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.