Deep learning for galaxy surface brightness profile fitting

Understand

Numerous ongoing and future large area surveys (e.g.

• DES, EUCLID, LSST, WFIRST), will increase by several orders of magnitude the volume of data that can be exploited for galaxy morphology studies.
• The full potential of these surveys can only be unlocked with the development of automated, fast and reliable analysis methods.
• In this paper we present DeepLeGATo, a new method for two-dimensional photometric galaxy profile modeling, based on convolutional neural networks.