Stellar formation rates in galaxies using Machine Learning models

Date Issued
2018
Author(s)
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Longo, Giuseppe
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.
Coverage
ESANN 2018 - Proceedings 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Start page
arXiv:1805.06338
Conferenece
ESANN 2018 : European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Conferenece place
Bruges
Conferenece date
25-27April, 2018
Uri
Url
Ads BibCode
2018arXiv180506338D
Rights
open.access
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DelliVeneriEtAl-1805.06338.pdf

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