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arXiv · 2002.03578

Predicting star formation properties of galaxies using deep learning

Shraddha Surana·Yogesh Wadadekar·Omkar Bait·Hrushikesh Bhosle
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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.

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Shraddha Surana, Yogesh Wadadekar, Omkar Bait, Hrushikesh Bhosle. 2020-02-10. Predicting star formation properties of galaxies using deep learning. https://doi.org/10.1093/mnras/staa537

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