Galaxy Formation and Evolution via Phase-temporal Clustering with FuzzyCat ◦ AstroLink

William H. Oliver, Tobias Buck

Abstract:

We demonstrate how the composition of two unsupervised clustering algorithms,
AstroLink and FuzzyCat, makes for a powerful tool when studying galaxy for-
mation and evolution. AstroLink is a general-purpose astrophysical clustering al-
gorithm built for extracting meaningful hierarchical structure from point-cloud data
defined over any feature space, while FuzzyCat is a generalised soft-clustering
algorithm that propagates the dynamical effects of underlying data processes
into a fuzzy hierarchy of stable fuzzy clusters. Their composition, FuzzyCat
◦AstroLink, can therefore identify a fuzzy hierarchy of astrophysically- and
statistically-significant fuzzy clusters within any point-based data set whose repre-
sentation is subject to changes caused by some underlying process. Furthermore,
the pipeline achieves this without relying upon strong assumptions about the data,
the change process, the number/importance of specific structure types, or much
user input – thereby making itself applicable to a wide range of fields in the phys-
ical sciences. We find that for the task of structurally decomposing simulated
galaxies into their constituents, our context-agnostic approach has a substantial
impact on the diversity and completeness of the structures extracted as well as on
their relationship within the broader galactic structural hierarchy – revealing dwarf
galaxies, infalling groups, stellar streams (and their progenitors), stellar shells,
galactic bulges, and star-forming regions.