Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection

TL;DR

This paper introduces a contrastive self-supervised convolutional autoencoder that improves detection of core-collapse supernova gravitational-wave signals, outperforming traditional methods and generalizing better to unseen waveforms.

Contribution

The authors develop a novel contrastive self-supervised autoencoder that enhances gravitational-wave detection for supernovae, reducing noise influence and improving generalization.

Findings

Achieves comparable performance to supervised CNNs.

Outperforms conventional autoencoders in detection accuracy.

Generalizes better to unseen supernova waveform families.

Abstract

Gravitational-wave astronomy has opened a direct observational window onto compact-object dynamics, strong-field gravity, and cosmology. Among the transient sources accessible through this window, core-collapse supernovae (CCSNe) are uniquely valuable because their signals can probe the engine of stellar collapse, proto-neutron-star dynamics, and explosion asymmetries, yet their weak, stochastic, and model-dependent waveforms remain difficult to detect. In this work, we develop a contrastive self-supervised convolutional autoencoder (CS-CAE) for CCSNe gravitational-wave signal detection. The method combines a convolutional autoencoder (CAE), a noise-centered latent regularizer, and a projection head trained with a contrastive objective. This design encourages independent noisy realizations of the same CCSNe signal to be mapped to nearby latent representations, thereby reducing the influence of random noise fluctuations. CS-CAE achieves performance comparable to a supervised convolutional neural network while clearly outperforming a conventional CAE baseline, and generalizes better to unseen numerical CCSNe waveform families. Under the Einstein Telescope (ET) detector configuration, the method achieves an effective sensitive distance of approximately 120 kpc and shows improved separation of CCSNe signals from stationary noise and transient glitches in the low-false-alarm regime. These results highlight the potential of CS-CAE as a robust and less template-dependent framework for CCSNe gravitational-wave searches.