Speaker
Description
We present here a data exploration tool designed to enhance the study of astrophysical objects by integrating traditional hierarchical clustering with graph-based community detection algorithms. This new tool allows in-depth analysis of the distributions of observables across astrophysical catalogs, while overcoming common challenges arising from the use of clustering algorithms that are not designed for population studies, such as noisy variables and sparse observations in parameter space. We demonstrate its effectiveness with examples from catalogues of solar flares, gamma-ray bursts, compact binary mergers, and active galactic nuclei. Finally, we discuss the most representative features that characterise the identified subpopulations to allow for qualitative analysis and model development.
