2026/03/30 by S. S. Mirzoyan, S S Mirzoyan, A. Avagyan +3
Physics and Astronomy · #Galaxies: Formation, Evolution, Phenomena #Astronomy and Astrophysical Research #Astrophysics and Star Formation Studies
paper · pdf · doi:10.1093/mnras/stag613
ABSTRACT Understanding the prevalence of disc-like galaxies at very high redshifts is crucial for constraining the early formation of angular momentum-supported structures. The advent of James Webb Space Telescope (JWST) now permits rest-frame UV and optical morphological studies deep into cosmic epochs where discs have traditionally been considered uncommon. We apply an identical denoising VAE → GCNN classification pipeline to multifilter JWST/NIRCam cutouts in order to obtain homogeneous, morphology-based disc 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 Group Convolutional Neural Networks (GCNN) classifier is applied to the denoised cutouts to distinguish disc-like galaxies from non-discs. We determine the fraction of disc-like galaxies as ≃ 0.34 for a sample of JWST 100 galaxies over the redshift range 4 \lesssim z \lesssim 7.7, also in dependence on the galaxy mass range. Our GCNN-based morphological analysis indicates that disc-like systems constitute a significant fraction of the considered high-redshift population and underscore the importance of such studies for the models of disc formation in the first billion years.