We present a deep learning model to predict the r-band bulge-to-total luminosity ratio (B/T)
of nearby galaxies using their multiband JPEG images alone. Our Convolutional Neural …

Aims. We explore machine learning techniques to forecast the star-formation rate, stellar
mass, and metallicity across galaxies with redshifts ranging from 0.01 to 0.3. Methods …

Context. One of the most important properties of a galaxy is the total stellar mass, or
equivalently the stellar mass-to-light ratio (M/L). It is not directly observable, but can be …

Conventional galaxy mass estimation methods suffer from model assumptions and
degeneracies. Machine learning (ML), which reduces the reliance on such assumptions, can …

The increasing size and complexity of data provided by both ongoing and planned galaxy
surveys greatly contribute to our understanding of galaxy evolution. Deep learning methods …

Aims. We explore machine learning techniques to forecast star formation rate, stellar mass,
and metallicity across galaxies with redshifts ranging from 0.01 to 0.3. Methods. Leveraging …

Abstract The galaxy Luminosity Function (LF) is a key observable for galaxy formation,
evolution studies and for cosmology. In this work, we propose a novel technique to forward …

Measuring the structural parameters (size, total brightness, light concentration, etc.) of
galaxies is a significant first step towards a quantitative description of different galaxy …

Abstract We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to
estimate morphological parameters and associated uncertainties for∼ 8 million galaxies in …

The estimation of the bulge and disk massses, the main baryonic components of a galaxy,
can be performed using various approaches, but their implementation tend to be …