Heidelberg 2022 – wissenschaftliches Programm
T 45.3: Vortrag
Dienstag, 22. März 2022, 16:45–17:00, T-H30
Classification of Fermi-LAT blazars with Bayesian neural networks — Anja Butter1, •Thorben Finke2, Felicitas Keil2, Michael Krämer2, and Silvia Manconi2 — 1Institut für Theoretische Physik, Universität Heidelberg, Germany — 2Institute for Theoretical Particle Physics and Cosmology, RWTH Aachen University, Germany
We apply Bayesian neural networks on the classification of γ-ray sources within the Fermi-LAT catalog. We focus on blazar candidates and their sub-classification into BL Lacertae and Flat Spectrum Radio Quasars. We explore the correspondence between conventional dense and Bayesian neural networks and the effect of data augmentation. We find that Bayesian neural networks provide a robust classifier with reliable uncertainty estimates and are particularly well suited for classification problems that are based on comparatively small and imbalanced data sets. The results of our blazar candidate classification are valuable input for population studies aimed at constraining the blazar luminosity function and to guide future observational campaigns.