Disk-like galaxies at 4 ≲ <i>z</i> ≲ 7.7: JWST/NIRCam morphologies revealed by denoising VAE-GCNN classification

Abstract Understanding the prevalence of disk-like galaxies at very high redshifts is crucial for constraining the early formation of angular momentum-supported structures. The advent of JWST now permits rest-frame UV and optical morphological studies deep into cosmic epochs where disks have traditionally been considered uncommon. We apply an identical denoising VAE → GCNN classification pipeline to multi-filter JWST/NIRCam cutouts in order to obtain homogeneous, morphology-based disk fractions across the sample. Our approach comprises two steps: (i) a U-Net Variational Autoencoder (VAE) is trained to remove astrophysical and instrumental contaminants while preserving intrinsic morphology, and (ii) a rotation - and reflection - equivariant GCNN classifier is applied to the denoised cutouts to distinguish disk-like galaxies from non-disks. We determine the fraction of disk-like galaxies as ≃ 0.34 for a sample of JWST 100 galaxies over the redshift range 4 ≲ z ≲ 7.7, also in dependence on the galaxy mass range. Our GCNN-based morphological analysis indicates that disk-like systems constitute a significant fraction of the considered high-redshift population and underscore the importance of such studies for the models of disk formation in the first billion years.