Speaker
Description
Active Galactic Nuclei (AGNs), found at the centers of galaxies, harbor supermassive black holes that emit immense energy across the electromagnetic spectrum. Traditionally, AGNs are classified based on factors like orientation (Type 1 vs. Type 2), radio emission (Radio-Loud vs. Radio-Quiet), or the presence of jets (Jetted vs. non-Jetted). However, these classifications may not fully capture AGN complexity. In this study, we employ unsupervised machine learning, specifically Uniform Manifold Approximation and Projection (UMAP), to explore AGN structure based solely on their X-ray properties. Our dataset comprises a large number of AGNs detected in X-ray surveys. By ignoring any pre-existing classifications, we seek hidden patterns within the high-dimensional X-ray parameter space. Initial findings reveal distinct clusters within the data. These clusters may represent different physical characteristics or evolutionary stages. Our next step involves correlating these clusters with established AGN properties: luminosity, supermassive black hole mass, accretion rate, and radio emission. This comparison could illuminate the underlying physical mechanisms behind observed X-ray signatures. Moreover, we explore the novel idea of classifying AGNs based on cluster membership alone. If successful, this approach may provide another dimension to AGN characterization and deepen our understanding of their diverse nature.
