Heidelberg 2022 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 45: Gamma Astronomy 2

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.