2021/06/15 by S. Germani, G. Tosti, . Lubrano +4
Physics and Astronomy · #Artificial intelligence #Artificial neural network #Astrophysics #Astrophysics and Cosmic Phenomena #Blazar #Computer science #Equipartition theorem #Feature (linguistics) #Fermi Gamma-ray Space Telescope #Galaxy #Gamma ray #Linguistics #Neutrino Physics Research #Outlier #Pattern recognition (psychology) #Physics #Quasar #Radio Astronomy Observations and Technology #astro-ph.HE
paper · pdf · doi:10.1093/mnras/stab1748
10 pages, 11 figures
arxiv created 2021/06/15 · openalex publication_date 2021/06/17 · arxiv updated 2021/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.