4 papers
Enhancing Galaxy Classification with U-Net Variational Autoencoders. III. Disk-like Galaxy Identification in JWST Samples of up to redshift 4
Sergey S. Mirzoyan, Arno Avagyan
In this third study of the series, we extend our U-Net Variational Autoencoder-based galaxy classification framework to a significantly larger JWST sample spanning the redshift ran…
Disk-like galaxies at 4 < z < 7.7 : JWST/NIRCam morphologies revealed by denoising VAE-GCNN classification
S. S. Mirzoyan, A. Avagyan, V. G. Gurzadyan
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 JW…
Enhancing Galaxy Classification with U-Net Variational Autoencoders. II. JWST High Redshift Galaxy Sample
Sergey Mirzoyan
Building on our previous work, we apply a U-Net Variational Autoencoder (VAE) framework to denoise galaxy images from the James Webb Space Telescope (JWST) and enhance morphologica…
Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising
Sergey Mirzoyan
AI-enhanced approaches are becoming common in astronomical data analysis, including in the galaxy morphological classification. In this study we develop an approach that enhances g…