Multivariate approaches to classification in extragalactic astronomy

Frontiers in Astronomy and Space Sciences

Chattopadhyay, Asis K.²  Fraix-Burnet, Didier⁴  Thuillard, Marc⁶ 

[1] Astrophysique de Grenoble, Grenoble, France;Astrophysique de Grenoble, University Grenoble Alpes, Grenoble, France;Centre National de la Recherche Scientifique, Institut de PlanéInstitut de PlanéLa Colline, St-Blaise, Switzerland;tologie et d'

关键词: clustering;    Classification;    Galaxies;    Multivariate analysis.;    phylogenetic methods;   

DOI  :  10.3389/fspas.2015.00003

学科分类：空间科学

来源: Frontiers

【 摘 要 】

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.

【 授权许可】

CC BY