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dc.contributor.advisorRodríguez Gil, Pablo 
dc.contributor.advisorHuertas Company, Marc
dc.contributor.authorLópez Morales, Alejandro
dc.contributor.otherMáster Universitario en Astrofísica
dc.date.accessioned2021-04-16T14:07:03Z
dc.date.available2021-04-16T14:07:03Z
dc.date.issued2021
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/22830
dc.description.abstractThe use of artificial intelligence and more precisely of Convolutional Neural Networks for the morphological classification of galaxies has been shown to be extremely reliable. In the last years, detailed kinematic studies of local early-type galaxies have revealed that a significant fraction of them seem to be rotationally supported. However, they seem to present no morphological signatures in their stellar morphology. In this work we apply machine learning to investigate the relation between kinematics and morphology of early-type galaxies. We use to that purpose SDSS imaging and spectroscopy from Manga together with simulated galaxies from the TNG100 cosmological simulation. We show that Neural Networks can distinguish fast and slow rotating early-type galaxies with 78% accuracy based exclusively on their stellar morphology even if no apparent signatures are visible to the human eyeen
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rightsLicencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
dc.titleInvestigating the morphologies of slow and fast rotators with deep learningen
dc.typeinfo:eu-repo/semantics/masterThesis


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Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
Except where otherwise noted, this item's license is described as Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)