most citedDeciphering galaxy images using machine vision -- Combining variational autoencoder and principal component analysis for feature extraction

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astro-ph.GA2025★ 1 cited

Deciphering galaxy images using machine vision -- Combining variational autoencoder and principal component analysis for feature extraction

Samuel Howie, Ting-Yun Cheng, Carlton M. Baugh

Here, we present a machine vision approach, combining a VAE framework with PCA, to decipher galaxy images. Using mock gri-band images from the EAGLE simulation, the VAE finds that…

astro-ph.GA2025

Explaining JWST counts with galaxy formation models

Giorgio Manzoni, Tom Broadhurst, Jeremy Lim +18

A distinct power-law break is apparent m_AB approximately 21 in the deep Near-Infrared PEARLS-JWST galaxy counts. The break becomes more pronounced at longer wavelengths, with the…