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

Understand

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.