Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12386/28798
Title: | Star formation rates for photometric samples of galaxies using machine learning methods | Authors: | DELLI VENERI, MICHELE CAVUOTI, STEFANO BRESCIA, Massimo Longo, G. RICCIO, GIUSEPPE |
Issue Date: | 2019 | Journal: | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | Number: | 486 | Issue: | 1 | First Page: | 1377 | Abstract: | Star formation rates (SFRs) are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised machine learning models. We demonstrate that, with such approach, accurate multiband photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky Survey Data Release 7. The catalogue will be made available through the Vizier facility. | URI: | http://hdl.handle.net/20.500.12386/28798 | URL: | https://doi.org/10.1093/mnras/stz856 https://academic.oup.com/mnras/article-abstract/486/1/1377/5420450?redirectedFrom=fulltext |
ISSN: | 0035-8711 | DOI: | 10.1093/mnras/stz856 | Bibcode ADS: | 2019MNRAS.486.1377D | Fulltext: | open |
Appears in Collections: | 1.01 Articoli in rivista |
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DelliVeneristz856.pdf | Pdf editoriale | 4.31 MB | Adobe PDF | View/Open |
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