Artificial Neural Network Classification of 4FGL Sources

Abstract

The Fermi-LAT DR1 and DR2 4FGL catalogues feature more than 5000 gamma-ray sources of which about one fourth are not associated with already known objects, and approximately one third are associated with blazars of uncertain nature. We perform a three-category classification of the 4FGL DR1 and DR2 sources independently, using an ensemble of Artificial Neural Networks (ANNs) to characterise them based on the likelihood of being a Pulsar (PSR), a BL Lac type blazar (BLL) or a Flat Spectrum Radio Quasar (FSRQ). We identify candidate PSR, BLL and FSRQ among the unassociated sources with approximate equipartition among the three categories and select ten classification outliers as potentially interesting for follow up studies.

10 pages, 11 figures

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Cited by in corpus (4)

• Gradient boosting decision trees classification of blazars of uncertain type in the fourth Fermi-LAT catalog
• Classifications of Fermi-LAT unassociated sources in multiple machine learning methods
• A search for dark matter among Fermi-LAT unidentified sources with systematic features in Machine Learning
• Exploring the nature of Galactic unassociated sources detected by the Fermi-LAT