Accéder directement au contenu Accéder directement à la navigation
Nouvelle interface
Communication dans un congrès

Deep learning for studies of galaxy morphology

Abstract : Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide paramet-ric properties of Hubble Space Telescope like galaxies (half-light radii, Sérsic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We compare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.
Liste complète des métadonnées

Littérature citée [4 références]  Voir  Masquer  Télécharger
Contributeur : Etienne Decencière Connectez-vous pour contacter le contributeur
Soumis le : vendredi 9 juin 2017 - 09:43:09
Dernière modification le : samedi 22 octobre 2022 - 05:12:21
Archivage à long terme le : : dimanche 10 septembre 2017 - 12:41:40


Fichiers produits par l'(les) auteur(s)



D Tuccillo, Marc Huertas-Company, Etienne Decencière, Santiago Velasco-Forero. Deep learning for studies of galaxy morphology. IAU Symposium 325 on Astroinformatics, Oct 2016, Sorrente, Italy. pp.191 - 196, ⟨10.1017/S1743921317000552⟩. ⟨hal-01535506⟩



Consultations de la notice


Téléchargements de fichiers