# LIGO Core-Collapse Supernova Detection Using Convolutional Neural Networks

## TL;DR

This paper explores using convolutional neural networks to detect gravitational waves from core-collapse supernovae, improving detection rates with specific signal processing techniques.

## Contribution

The novel use of CNNs with time–frequency spectrograms for detecting gravitational waves from CCSNe is introduced and evaluated.

## Key findings

- CNNs achieve near 100% true positive rate for CCSNe GW events with SNR > 0.5.
- CNNs trained on STFT spectrograms perform better than those trained on Q-transform spectrograms at lower SNRs.

## Abstract

Core-collapse supernovae (CCSNe) remain a critical focus in the search for gravitational waves in modern astronomy. Their detection and subsequent analysis will enhance our understanding of the explosion mechanisms in massive stars. This paper investigates the use of convolutional neural networks (CNN) to enhance the detection of gravitational waves originating from CCSNe. We employ two time–frequency analysis techniques to generate spectrograms (training data): short-time Fourier transform (STFT) and Q-transform (QT). Two CNNs were trained independently on sets of spectrogram images of simulated CCSNe signals and advanced LIGO noise. The CNNs detect CCSNe signals based on their time–frequency representation. Both CNNs achieve a near 100% true positive rate for CCSNe GW events with a signal-to-noise ratio greater than 0.5 in our test set. Nevertheless, the CNN trained on the STFT spectrograms outperforms the one based on the Q-transform for SNRs below 0.5.