Multi-layered model-based characterisation of the local-Universe galaxy data from the GAMA survey
Understanding the formation and evolution of galaxy populations requires robust classification and characterisation techniques that jointly account for internal galaxy properties and environment. We analyse $5,306$ galaxies from the Galaxy And Mass Assembly (GAMA) survey, described by stellar mass, specific star formation rate, $u-r$ colour, half-light radius, Sérsic index, and a combined environmental measure given by the optimal density. Unlike distance-based unsupervised clustering methods, our framework provides a probabilistic characterisation of galaxy populations, accommodates heavy-tailed feature distributions, and captures dependence among observables through latent factors. We model the sample using a $t$-mixture of factor analysers with group-specific latent structures (M$t$FAD), and then apply model-estimated overlap-based syncytial clustering (MOBSynC) to merge weakly separated groups and recover higher-level population structure. The first stage identifies eight simple clusters. The third and the fourth groups lie on the red, low-star-forming sequence and correspond to environmentally quenched and mass-quenched systems, respectively, while the sixth group traces the massive end of the star-forming sequence, and the seventh group appears to represent a more heterogeneous population that may include transition objects. The remaining groups populate the low- to intermediate-mass blue sequence, including both compact and more extended star-forming galaxies. The second MOBSynC stage merges the simple clusters into two compound groups: a red sequence formed by the third and the fourth groups, and the rest merging to form a broad blue sequence. Our results show that the familiar red-blue bimodality of local galaxies contains additional physically meaningful substructure linked to quenching pathway, morphology, and environment.