Stellar formation rates in galaxies using Machine Learning models

2018/05/16 by Michele Delli Veneri, S. Cavuoti, Veneri, Michele Delli +7
Physics and Astronomy · Engineering · Mathematics · #Astronomy and Astrophysical Research #Astronomical Observations and Instrumentation #Statistical and numerical algorithms

paper · pdf · doi:10.48550/arxiv.1805.06338

Abstract

Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot match the needs of modern precision cosmology. We therefore propose a novel method to estimate SFRs for large samples of galaxies using a variety of supervised ML models.

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