Stellar formation rates in galaxies using Machine Learning models

TL;DR

This paper introduces a machine learning approach to estimate galaxy star formation rates efficiently, reducing reliance on time-consuming spectroscopic observations and enabling large-scale cosmological studies.

Contribution

It presents a novel supervised machine learning method for estimating galaxy SFRs, offering a faster alternative to traditional spectroscopic techniques.

Findings

ML models accurately predict SFRs from galaxy data

Reduces telescope time needed for SFR estimation

Enables analysis of large galaxy samples

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.