Classification of Fermi-LAT blazars with Bayesian neural networks

Abstract

The use of Bayesian neural networks is a novel approach for the classification of gamma-ray sources. We focus on the classification of Fermi-LAT blazar candidates, which can be divided into BL Lacertae objects and Flat Spectrum Radio Quasars. In contrast to conventional dense networks, Bayesian neural networks provide a reliable estimate of the uncertainty of the network predictions. We explore the correspondence between conventional 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.

22 pages, 10 figures. Minor changes, matches version accepted by JCAP

References in corpus (6)

• Active Galactic Nuclei: what's in a name?
• The Origin of the Extragalactic Gamma-Ray Background and Implications for Dark-Matter Annihilation
• The diffuse $\gamma$-ray background is dominated by star-forming galaxies
• Blazar Flaring Patterns (B-FlaP): Classifying Blazar Candidates of Uncertain type in the third Fermi-LAT catalog by Artificial Neural Networks
• 3FGLzoo. Classifying 3FGL Unassociated Fermi-LAT Gamma-ray Sources by Artificial Neural Networks
• An investigation on the factors affecting machine learning classifications in $\gamma$-ray astronomy