Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach

TL;DR

This paper presents a deep learning method using convolutional neural networks to detect and classify gravitational wave signals from supernovae, demonstrating promising detection capabilities with simulated data.

Contribution

The study introduces a CNN-based approach for supernova gravitational wave detection and classification, showing its effectiveness on simulated signals and potential for real detector data.

Findings

CNN can detect supernova GW signals buried in noise.

Detection probability exceeds 50% at 60 kpc for certain waveforms.

High detection accuracy (up to 93%) at 10 kpc for specific waveforms.

Abstract

We demonstrate the application of a convolutional neural network to the gravitational wave signals from core collapse supernovae. Using simulated time series of gravitational wave detectors, we show that based on the explosion mechanisms, a convolutional neural network can be used to detect and classify the gravitational wave signals buried in noise. For the waveforms used in the training of the convolutional neural network, our results suggest that a network of advanced LIGO, advanced VIRGO and KAGRA, or a network of LIGO A+, advanced VIRGO and KAGRA is likely to detect a magnetorotational core collapse supernovae within the Large and Small Magellanic Clouds, or a Galactic event if the explosion mechanism is the neutrino-driven mechanism. By testing the convolutional neural network with waveforms not used for training, we show that the true alarm probabilities are 52% and 83% at 60 kpc for waveforms R3E1AC and R4E1FC L. For waveforms s20 and SFHx at 10 kpc, the true alarm probabilities are 70% and 93% respectively. All at false alarm probability equal to 10%.