Segmenting proto-halos with vision transformers
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915982495186944 |
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| author | Alokda, Toka Porciani, Cristiano |
| author_facet | Alokda, Toka Porciani, Cristiano |
| contents | The formation of dark-matter halos from small cosmological perturbations generated in the early universe is a highly non-linear process typically modeled through N-body simulations. In this work, we explore the use of deep learning to segment and classify proto-halo regions in the initial density field according to their final halo mass at redshift z=0. We compare two architectures: a fully convolutional neural network (CNN) based on the V-Net design and a U-Net transformer. We find that the transformer-based network significantly outperforms the CNN across all metrics, achieving sub-percent error in the total segmented mass per halo class. Both networks deliver much higher accuracy than the perturbation-theory-based model \textsc{pinocchio}, especially at low halo masses and in the detailed reconstruction of proto-halo boundaries. We also investigate the impact of different input features by training models on the density field, the tidal shear, and their combination. Finally, we use Grad-CAM to generate class-activation heatmaps for the CNN, providing preliminary yet suggestive insights into how the network exploits the input fields. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_00049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Segmenting proto-halos with vision transformers Alokda, Toka Porciani, Cristiano Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition 85-08, 68T07 The formation of dark-matter halos from small cosmological perturbations generated in the early universe is a highly non-linear process typically modeled through N-body simulations. In this work, we explore the use of deep learning to segment and classify proto-halo regions in the initial density field according to their final halo mass at redshift z=0. We compare two architectures: a fully convolutional neural network (CNN) based on the V-Net design and a U-Net transformer. We find that the transformer-based network significantly outperforms the CNN across all metrics, achieving sub-percent error in the total segmented mass per halo class. Both networks deliver much higher accuracy than the perturbation-theory-based model \textsc{pinocchio}, especially at low halo masses and in the detailed reconstruction of proto-halo boundaries. We also investigate the impact of different input features by training models on the density field, the tidal shear, and their combination. Finally, we use Grad-CAM to generate class-activation heatmaps for the CNN, providing preliminary yet suggestive insights into how the network exploits the input fields. |
| title | Segmenting proto-halos with vision transformers |
| topic | Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition 85-08, 68T07 |
| url | https://arxiv.org/abs/2508.00049 |