Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.01376 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908746932813824 |
|---|---|
| author | Wang, Ao-Bo Yuan, Yong Cai, Hao Fan, Xi-Long |
| author_facet | Wang, Ao-Bo Yuan, Yong Cai, Hao Fan, Xi-Long |
| contents | The detection and reconstruction of gravitational waves from core-collapse supernovae (CCSN) present significant challenges due to the highly stochastic nature of the signals and the complexity of detector noise. In this work, we introduce a deep learning framework utilizing a ResNet-50 encoder pre-trained via supervised contrastive learning to classify CCSN signals and distinguish them from instrumental noise artifacts. Our approach explicitly optimizes the feature space to maximize intra-class compactness and inter-class separability. Using a simulated four-detector network (LIGO Hanford, LIGO Livingston, Virgo, and KAGRA) and realistic datasets injecting magnetorotational and neutrino-driven waveforms, we demonstrate that the contrastive learning paradigm establishes a superior metric structure within the embedding space, significantly enhancing detection efficiency. At a false positive rate of $10^{-4}$, our method achieves a true positive rate (TPR) of nearly $100\%$ for both rotational and neutrino-driven signals within a distance range of $10$--$200$~kpc, while maintaining a TPR of approximately $80\%$ at $1200$~kpc. In contrast, traditional end-to-end methods yield a TPR below $20\%$ for rotational signals at distances $\geq 200$~kpc, and fail to exceed $60\%$ for neutrino-driven signals even at a close proximity of $10$~kpc. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01376 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Classifying Core-Collapse Supernova Gravitational Waves using Supervised Contrastive Learning Wang, Ao-Bo Yuan, Yong Cai, Hao Fan, Xi-Long High Energy Astrophysical Phenomena The detection and reconstruction of gravitational waves from core-collapse supernovae (CCSN) present significant challenges due to the highly stochastic nature of the signals and the complexity of detector noise. In this work, we introduce a deep learning framework utilizing a ResNet-50 encoder pre-trained via supervised contrastive learning to classify CCSN signals and distinguish them from instrumental noise artifacts. Our approach explicitly optimizes the feature space to maximize intra-class compactness and inter-class separability. Using a simulated four-detector network (LIGO Hanford, LIGO Livingston, Virgo, and KAGRA) and realistic datasets injecting magnetorotational and neutrino-driven waveforms, we demonstrate that the contrastive learning paradigm establishes a superior metric structure within the embedding space, significantly enhancing detection efficiency. At a false positive rate of $10^{-4}$, our method achieves a true positive rate (TPR) of nearly $100\%$ for both rotational and neutrino-driven signals within a distance range of $10$--$200$~kpc, while maintaining a TPR of approximately $80\%$ at $1200$~kpc. In contrast, traditional end-to-end methods yield a TPR below $20\%$ for rotational signals at distances $\geq 200$~kpc, and fail to exceed $60\%$ for neutrino-driven signals even at a close proximity of $10$~kpc. |
| title | Classifying Core-Collapse Supernova Gravitational Waves using Supervised Contrastive Learning |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2601.01376 |