Classification of Fermi-LAT blazars with Bayesian neural networks
The use of Bayesian neural networks is a novel approach for the classification of γ-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 n...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
13 April 2022
|
| In: |
Journal of cosmology and astroparticle physics
Year: 2022, Issue: 4, Pages: 1-23 |
| ISSN: | 1475-7516 |
| DOI: | 10.1088/1475-7516/2022/04/023 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1088/1475-7516/2022/04/023
|
| Author Notes: | Anja Butter, Thorben Finke, Felicitas Keil, Michael Krämer, Silvia Manconi |
| Summary: | The use of Bayesian neural networks is a novel approach for the classification of γ-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. |
|---|---|
| Item Description: | Gesehen am 10.06.2022 |
| Physical Description: | Online Resource |
| ISSN: | 1475-7516 |
| DOI: | 10.1088/1475-7516/2022/04/023 |