Visualizing convolutional neural network for classifying gravitational waves from core-collapse supernovae

Abstract

In this study, we employ a convolutional neural network to classify gravitational waves originating from core-collapse supernovae. Training is conducted using spectrograms derived from three-dimensional numerical simulations of waveforms, which are injected onto real noise data from the third observing run of both Advanced LIGO and Advanced Virgo. To gain insights into the decision-making process of the model, we apply class activation mapping techniques to visualize the regions in the input image that are significant for the model's prediction. The class activation maps reveal that the model's predictions predominantly rely on specific features within the input spectrograms, namely, the $g$-mode and low-frequency modes. The visualization of convolutional neural network models provides interpretability to enhance their reliability and offers guidance for improving detection efficiency.

arXiv comment13 pages, 10 figures

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Cited by in corpus (7)

• Applications of machine learning in gravitational wave research with current interferometric detectors
• Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
• Classifying binary black holes from Population III stars with the Einstein Telescope: A machine-learning approach
• Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
• Parameter estimation of protoneutron stars from gravitational wave signals using the Hilbert-Huang transform
• Effects of multidimensional treatment of gravity in simulations on supernova gravitational waves
• Stochastic gravitational wave background due to core collapse resulting in neutron stars