Predicting star formation properties of galaxies using deep learning
This work addresses galaxy evolution studies by providing an alternative machine learning approach for astronomers, but it is incremental as it builds on existing methods.
The authors tackled predicting star formation properties of galaxies by using deep learning techniques to estimate stellar mass, star formation rate, and dust luminosity, comparing performance with a standard stellar population synthesis code.
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.