Multivariate Evolutionary Analyses in Astrophysics

TL;DR

This paper introduces Astrocladistics, a novel evolutionary-based multivariate analysis method for classifying galaxies and globular clusters, addressing interpretability issues in astrophysical data analysis.

Contribution

It presents Astrocladistics, an innovative approach incorporating evolutionary concepts into multivariate classification, improving interpretability in astrophysics.

Findings

Astrocladistics successfully classifies globular clusters and early-type galaxies.

Evolutionary scenarios enhance understanding of galaxy properties.

Method bridges gap between clustering and evolutionary analysis.

Abstract

The large amount of data on galaxies, up to higher and higher redshifts, asks for sophisticated statistical approaches to build adequate classifications. Multivariate cluster analyses, that compare objects for their global similarities, are still confidential in astrophysics, probably because their results are somewhat difficult to interpret. We believe that the missing key is the unavoidable characteristics in our Universe: evolution. Our approach, known as Astrocladistics, is based on the evolutionary nature of both galaxies and their properties. It gathers objects according to their "histories" and establishes an evolutionary scenario among groups of objects. In this presentation, I show two recent results on globular clusters and earlytype galaxies to illustrate how the evolutionary concepts of Astrocladistics can also be useful for multivariate analyses such as K-means Cluster Analysis.