Astrocladistics: Multivariate Evolutionary Analysis in Astrophysics

TL;DR

Astrocladistics introduces a phylogenetic approach to galaxy classification, leveraging multivariate analysis and continuous data to better understand galaxy evolution beyond traditional morphology-based schemes.

Contribution

This work adapts phylogenetic systematics to astrophysics, enabling the analysis of continuous galaxy data and incorporating evolutionary relationships into classification.

Findings

Successful application to galaxy and globular cluster samples

Extension of phylogenetic methods to continuous astrophysical data

Improved understanding of galaxy evolution patterns

Abstract

The Hubble tuning fork diagram, based on morphology and established in the 1930s, has always been the preferred scheme for classification of galaxies. However, the current large amount of data up to higher and higher redshifts asks for more sophisticated statistical approaches like multivariate analyses. Clustering analyses are still very confidential, and do not take into account the unavoidable characteristics in our Universe: evolution. Assuming branching evolution of galaxies as a 'transmission with modification', we have shown that the concepts and tools of phylogenetic systematics (cladistics) can be heuristically transposed to the case of galaxies. This approach that we call "astrocladistics", has now successfully been applied on several samples of galaxies and globular clusters. Maximum parsimony and distance-based approaches are the most popular methods to produce phylogenetic trees and, like most other studies, we had to discretize our variables. However, since astrophysical data are intrinsically continuous, we are contributing to the growing need for applying phylogenetic methods to continuous characters.