PoS - Proceedings of Science
Volume 372 - Artificial Intelligence for Science, Industry and Society (AISIS2019) - Day 1
Galaxy Morphology classification using CNN
J.A. Vázquez-Mata,* H.M. Hernandez-Toledo, L.C. Mascherpa
*corresponding author
Full text: pdf
Pre-published on: July 10, 2020
Published on:
Abstract
Galaxy morphology is one of the most important parameters to understand the assembly and evolution of galaxies in the universe. The most used classification nowadays was proposed by Hubble (1926). This classification is based on the present of disks-, arms-, bulges-like structures, and is carried out mostly by eye identification. Thanks to the upcoming large telescopes and surveys, the new catalogues will contain millions of galaxies, making impossible to carry out a visual classification. Then, convolutional neural networks (CNN) start to play an important role to classify galaxies automatically. In this work I summarise the most recent results (including ours) using machine learning techniques to classify galaxies and future projects.
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.