Stellar formation rates in galaxies using Machine Learning models
Instrumentation and Methods for Astrophysics
2019-01-24 v3 Astrophysics of Galaxies
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.
Cite
@article{arxiv.1805.06338,
title = {Stellar formation rates in galaxies using Machine Learning models},
author = {Michele Delli Veneri and Stefano Cavuoti and Massimo Brescia and Giuseppe Riccio and Giuseppe Longo},
journal= {arXiv preprint arXiv:1805.06338},
year = {2019}
}
Comments
ESANN 2018 - Proceedings, ISBN-13 9782875870483