Classification of Fermi-LAT blazars with Bayesian neural networks

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.

Automated summary

The use of Bayesian neural networks provides a novel approach for classifying gamma-ray sources, specifically focusing on the classification of Fermi-LAT blazar candidates. Unlike conventional networks, Bayesian neural networks give reliable uncertainty estimates, making them suitable for small and imbalanced data sets. The results of this study are important for understanding the blazar luminosity function and guiding future observational campaigns.