Predicting star formation properties of galaxies using deep learning

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 models have been used to obtain best fit parameters that characterise star formation in galaxies. As multiband flux measurements become available for thousands of galaxies, an alternative approach to characterising 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 characterise the performance of our deep learning models through comparisons with outputs from a standard stellar population synthesis code.

9 pages, 13 figures, 3 Tables, Accepted for publication in MNRAS

References in corpus (14)

• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• Properties of galaxies reproduced by a hydrodynamic simulation
• A simple model to interpret the ultraviolet, optical and infrared emission from galaxies
• Stellar Masses and SFRs for 1M Galaxies from SDSS and WISE
• Machine Learning in Astronomy: a practical overview
• A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description
• Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees
• Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
• On the interdependence of galaxy morphology, star formation, and environment in massive galaxies in the nearby Universe
• Star formation rates and stellar masses from machine learning
• Automated physical classification in the SDSS DR10. A catalogue of candidate Quasars
• The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples
• Star Formation Rates for photometric samples of galaxies using machine learning methods
• Machine Vision and Deep Learning for Classification of Radio SETI Signals