Artificial Neural Network classification of 4FGL sources. Issue 4 (24th June 2021)

• Record Type: Journal Article
• Authors: Germani, S; Tosti, G; Lubrano, P; Cutini, S; Mereu, I; Berretta, A
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 505:Issue 4(2021)
• Page Start: 5853
• Page End: 5861
• Publication Date: 2021-06-24
• Subjects: methods: statistical -- pulsars: general -- galaxies: active -- gamma-rays: general; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stab1748
• Languages: English
• ISSNs: 0035-8711

Abstract

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 characterize 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 10 classification outliers as potentially interesting for follow-up studies.