Speaker
Description
Understanding the star formation and morphological properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditional approaches to characterize these properties require a significant amount of human intervention and are computationally expensive, particularly for large samples of galaxies. Deep learning models have the potential to overcome these shortcomings. In this work, we present two deep learning models to predict : 1) stellar mass, star formation rate, and dust luminosity 2) r-band bulge-to-total luminosity ratio (B/T) of nearby galaxies. Our models are able to give an accurate estimation of these properties significantly faster than traditional approaches. The proposed deep learning approach could potentially save a tremendous amount of time, effort, and computational resources, particularly in the era of next-generation sky surveys such as the Legacy Survey of Space and Time (LSST) and the Euclid sky survey which will produce extremely large samples of galaxies.
