Stellar formation rates in galaxies using Machine Learning models

DELLI VENERI, MICHELEMICHELEDELLI VENERICAVUOTI, STEFANOSTEFANOCAVUOTIBRESCIA, MassimoMassimoBRESCIARICCIO, GIUSEPPEGIUSEPPERICCIOLongo, GiuseppeGiuseppeLongo2020-10-052020-10-052018978-2-87587-048-3http://hdl.handle.net/20.500.12386/27588Global 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.ELETTRONICOenStellar formation rates in galaxies using Machine Learning modelsConference paper2-s2.0-85069430205http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=67641&copyownerid=821122018arXiv180506338DFIS/05 - ASTRONOMIA E ASTROFISICAERC sectors::Physical Sciences and Engineering::PE9 Universe sciences: astro-physics/chemistry/biology; solar systems; stellar, galactic and extragalactic astronomy, planetary systems, cosmology, space science, instrumentation