RT info:eu-repo/semantics/masterThesis
T1 Investigating the morphologies of slow and fast rotators with deep learning
A1 López Morales, Alejandro
A2 Máster Universitario en Astrofísica
AB The use of artificial intelligence and more precisely of Convolutional Neural Networks forthe morphological classification of galaxies has been shown to be extremely reliable. In thelast 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 nomorphological signatures in their stellar morphology. In this work we apply machine learningto investigate the relation between kinematics and morphology of early-type galaxies. We useto that purpose SDSS imaging and spectroscopy from Manga together with simulated galaxiesfrom the TNG100 cosmological simulation. We show that Neural Networks can distinguishfast and slow rotating early-type galaxies with 78% accuracy based exclusively on their stellarmorphology even if no apparent signatures are visible to the human eye
YR 2021
FD 2021
LK http://riull.ull.es/xmlui/handle/915/22830
UL http://riull.ull.es/xmlui/handle/915/22830
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 30-dic-2024