Predicting star formation properties of galaxies using deep learning
Shraddha Surana, Yogesh Wadadekar, Omkar Bait, Hrushikesh Bhosale
Abstract
ABSTRACT Understanding the star formation properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditionally, stellar population synthesis (SPS) models have been used to obtain best-fitting parameters that characterize star formation in galaxies. As multiband flux measurements become available for thousands of galaxies, an alternative approach to characterizing star formation using machine learning becomes feasible. In this work, we present the use of deep learning techniques to predict three important star formation properties – stellar mass, star formation rate, and dust luminosity. We characterize the performance of our deep learning models through comparisons with outputs from a standard SPS code.