Using Machine Learning to detect growing supermassive black holes.

The aim of this project was to assess the effectiveness of T-distributed Stochastic Neighbour Embedding (t-SNE) in identifying and classifying Active Galactic Nuclei (AGN) utilizing data from the GAMA survey, along with ancillary datasets from radio, X-ray and infrared surveys. In spite of the application of several diagnostic measures to understand the separation of the dataset in clusters, no substructure was identified using the assessment methods, suggesting the need for further work and research, and perhaps the use of more features to effectively identify these populations.