Galaxies exhibit a wide variety of morphologies which are strongly related to their star formation histories. Having large samples of morphologically classified galaxies is fundamental to understand their formation and evolution. In this talk, I will review my research related to deep learning algorithms for morphological classification of galaxies which have resulted in the release of morphological catalogues for large international surveys such as SDSS, MaNGA or Dark Energy Survey. I will describe the methodology, based on  supervised learning and convolutional neural networks (CNN). The main disadvantage of such approach is the need of large labelled training samples which we overcome by applying transfer learning or by ‘emulating’ the faint galaxy population. 

Helena Domínguez Sánchez is a research fellow astrophysicist at Institute of Space Sciences (ICE-CSIC) trying to understand how and why the properties of galaxies have changed across the history of the Universe. During the last years, she has pioneered the use of Deep Learning techniques in astronomy. She  did her PhD in Bologna (2009-2012) and the she had several post-docs positions in UCM (Madrid), Paris Observatoire and University of Pennsylvania (USA). She is currently visiting the Instituto de Astrofísica de Canarias (IAC, Tenerife) for a semester and she just accepted a tenure track position at Centro de Estudios de Física del Cosmos de Aragón (CEFCA, Teruel), starting September 2022.