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A fundamental bimodality of galaxies in the local Universe is apparent inmany of the features used to describe them. Multiple sub-populations existwithin this framework, each representing galaxies following distinctevolutionary pathways. Accurately identifying and characterising thesesub-populations requires that a large number of galaxy features be analysedsimultaneously. Future galaxy surveys such as LSST and Euclid will yield datavolumes for which traditional approaches to galaxy classification will becomeunfeasible. To address this, we apply a robust $k$-means unsupervisedclustering method to feature data derived from a sample of 7338 local-Universegalaxies selected from the Galaxy And Mass Assembly (GAMA) survey. This allowsus to partition our sample into $k$ clusters without the need for training onpre-labelled data, facilitating a full census of our high dimensionalityfeature space and guarding against stochastic effects. We find that the localgalaxy population natively splits into $2$, $3$, $5$ and a maximum of $6$sub-populations, with each corresponding to a distinct ongoing evolutionarymechanism. Notably, the impact of the local environment appears strongly linkedwith the evolution of low-mass ($M_{*} < 10^{10}$ M$_{\odot}$) galaxies, withmore massive systems appearing to evolve more passively from the blue cloudonto the red sequence. With a typical run time of $\sim3$ minutes per value of$k$ for our galaxy sample, we show how $k$-means unsupervised clustering is anideal tool for future analysis of large extragalactic datasets, being scalable,adaptable, and providing crucial insight into the fundamental properties of thelocal galaxy population.

Reproducible $k$-means clustering in galaxy feature data from the GAMA survey