Unsupervised Methods in Morphological Classification Applied to Galaxy Evolution

Galaxy morphology offers significant insights into the evolutionary pathways and underlying physics of galaxies. As astronomical data grow with surveys such as CEERS/JWST, there is a need for tools to classify and analyse the vast numbers of galaxies that will be observed. In this presentation I will introduce a novel classification technique blending unsupervised clustering based on morphological metrics with the scalability of supervised Convolutional Neural Networks. We present a comparative analysis between the well-known CAS (Concentration, Asymmetry, and Smoothness) metrics and our newly proposed EGG (Entropy, Gini, and Gradient Pattern Analysis). Our choice of the EGG system stems from its separation-oriented metrics, maximizing morphological class contrast. Applying our methodology to the Sloan Digital Sky Survey data, we obtain ∼95% of Overall Accuracy of purely unsupervised classification and when we replicate T-Type and visually classified galaxy catalogues with accuracy of ∼88% and ∼89%, respectively. Furthermore, the application to Hubble Space Telescope data heralds the potential for unsupervised exploration of a higher redshift range. A notable achievement is our ∼95 per cent accuracy in unsupervised classification, a result that rivals when juxtaposed with Traditional Machine Learning and closely trails when compared to Deep Learning benchmarks. Some preliminary results using CANDELS data will be presented and discussed.