Multivariate Approaches to Classification in Extragalactic Astronomy

TL;DR

This paper reviews multivariate statistical methods for classifying galaxies, emphasizing their importance in handling large datasets and advancing astrophysical understanding beyond traditional classification systems.

Contribution

It provides a comprehensive overview of multivariate approaches in galaxy classification, highlighting their role in modern astrophysics and data-driven analysis.

Findings

Multivariate methods enhance galaxy classification accuracy.

These approaches reveal new insights into galaxy physics and evolution.

Multivariate analyses are crucial for handling large astrophysical datasets.

Abstract

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.