LIGO Core-collapse Supernova Detection Using Convolutional Neural Networks
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Authors
Pan, Zhicheng
Zahraoui, El Mehdi
Maturana-Russel, Patricio
Cabrera-Guerrero, Guillermo
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Journal Article
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MDPI AG
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.Description
Keywords
4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences, Machine Learning and Artificial Intelligence, 0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, Analytical Chemistry, 3103 Ecology, 4008 Electrical engineering, 4009 Electronics, sensors and digital hardware, 4104 Environmental management, 4606 Distributed computing and systems software, core-collapse supernovae, CNN, Q-transform, aLIGO
Source
Sensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 26(6), 1749-1749. doi: 10.3390/s26061749
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© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.