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. © ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Stellar formation rates in galaxies using Machine Learning models / Delli Veneri, M.; Cavuoti, S.; Brescia, M.; Riccio, G.; Longo, G.. - (2018), pp. 333-338. (Intervento presentato al convegno 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018; Bruges; Belgium; 25 April 2018 through 27 April 2018; Code 149253 tenutosi a Bruges, Belgium nel 25-27 April 2018).
Stellar formation rates in galaxies using Machine Learning models
Delli Veneri, M.
;Cavuoti, S.;Brescia, M.;Longo, G.
2018
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. © ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.| File | Dimensione | Formato | |
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