Segmenting proto-halos with vision transformers
For cosmologists, this provides a more accurate method to predict halo formation from initial conditions, especially for low-mass halos and boundary reconstruction.
This work uses deep learning to segment proto-halo regions in the initial density field and classify them by final halo mass. The transformer-based network achieves sub-percent error in total segmented mass per halo class, significantly outperforming both a CNN and the perturbation-theory model PINOCCHIO.
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.