I was delighted to see a new paper deposited this November 2020 into ArXiv:
A phylogenetic analysis of galaxies in the Coma Cluster and the field: a new approach to galaxy evolution by Martinez-Marin et al.
This paper uses the NJ algorithm to perform a chemical tagging analysis of a few hundreds of galaxies within and outside the Coma cluster. This is very interesting work, well done, opening promising follow-ups.
In 2018, we addressed the chemical tagging issue on stars in open clusters in our own Galaxy, the Milky Way. The use of a phylogenetic approach is rather logical since stars are born is large groups sharing the same chemical composition, but evolving differently according to their masses and different trajectories. Hence all stars of the same family diverged both in the chemical space and the real space.
For galaxies, this is basically the same logic although more complicated because of many populations of stars and many interactions and merging with other galaxies. Nevertheless, the chemical indicators are probably the best observables to represent evolutionary stages, hence to be “characters” in the cladistics sense.
One very interesting result of this paper confirms our finding on the WINGS dataset published in 2019. Like us, they used galaxies in the field, that are not gravitationally bounded to a cluster, to compare the phylogenetic hypothesis with that of galaxies in clusters. The common result is that the field galaxies are less diversified since the trees are less structured.
In other words, the environment of clusters triggers the appearance of new “species”, very certainly because of a higher probability of encounters, perturbations, and at a much higher rate than in the field.
Not only this is for me a great joy to see other colleagues engage themselves into astrocladistics.
But this is really a huge satisfaction to get convergent physical outcomes since this reinforces the validity, the reliability, the reproducibility of the phylogenetic approach in astrophysics.
If you ever wonder, I have not at all abandoned astrocladistics. I devoted the last years to unsupervised clustering in order to be able to tackle large samples with cladistics. I am ready now to start several new analyses. More to come!
Astrocladistics 