Related papers: Classification of the \emph{Fermi}-LAT Blazar Cand…
The Fermi-LAT has detected more than 3000 sources in the GeV $\gamma$-ray regime. The majority are extra-galactic and these sources are dominated by blazars. However, $\sim28$ per cent of the sources in Fermi 3LAC are listed as blazar…
We utilize machine learning methods to distinguish BL Lacertae objects (BL Lac) from Flat Spectrum Radio Quasars (FSRQ) within a sample of likely X-ray blazar counterparts to Fermi 3FGL unassociated gamma-ray sources. From our previous…
Machine learning (ML) and deep learning (DL) techniques are increasingly used across astrophysics, enabled by the growing availability of data and improved acquisition methods. These approaches now support tasks from redshift estimation to…
Nearly 50% of all sources detected by the Fermi Large Area Telescope are classified as blazars or blazar candidates, one of the most elusive classes of active galaxies. Additional blazars can also be hidden within the sample of unidentified…
Blazars, one of the most extreme class of active galaxies, constitute so far the largest known population of $\gamma$-ray sources and their number is continuously growing in the Fermi catalogs. However in the latest release of the Fermi…
Despite the fact that blazars constitute the rarest class among active galactic nuclei (AGNs) they are the largest known population of associated $\gamma$-ray sources. Many of the $\gamma$-ray objects listed in the Fermi-Large Area…
A significant fraction (~30%) of the high-energy gamma-ray sources listed in the second Fermi LAT catalog (2FGL) are still of unknown origin, being not yet associated with counterparts at low energies. We recently developed a new…
In the recently published 3FGL catalogue, the Fermi/LAT collaboration reports the detection of $\gamma$-ray emission from 3034 sources obtained after four years of observations. The nature of 1010 of those sources is unknown, whereas 2023…
The third Fermi source catalog lists 3033 gamma-ray sources above $4\sigma$ significance. More than 30% are classified as either unidentified/unassociated Gamma-ray sources (UGSs), with about 20% classified as Blazar candidates of uncertain…
One of the main scientific objectives of the ongoing Fermi mission is unveiling the nature of the unidentified gamma-ray sources (UGSs). Despite the large improvements of Fermi in the localization of gamma-ray sources with respect to the…
The Fermi-LAT unassociated sources represent some of the most enigmatic gamma-ray sources in the sky. Observations with the Swift-XRT and -UVOT telescopes have identified hundreds of likely X-ray and UV/optical counterparts in the…
Context. Based on their overwhelming dominance among associated Fermi gamma ray catalogue sources, it is expected that a large fraction of the unidentified Fermi objects are blazars. Through crossmatching between the positions of…
Approximately one-third of the gamma-ray sources in the third Fermi-LAT catalog are unidentified or unassociated with objects at other wavelengths. Observations with Swift-XRT have yielded possible counterparts in $\sim$30% of these source…
A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources…
The Fermi/LAT telescope is an efficient blazar-detector in the MeV/GeV range. More than 1100 (900) blazars detected above 100 MeV (10 GeV) are clearly associated to BL Lacertae or Flat Spectrum Radio Quasar objects in the Fermi/LAT 3FGL…
Nearly one-third of the sources in the $Fermi$-LAT catalogs lack a lower energy counterpart, hence being referred as unidentified/unassociated gamma-ray sources (UGSs). In order to firmly classify them, dedicated multifrequency follow-up…
Despite the growing number of gamma-ray sources detected by Fermi-LAT, about one third of the sources in each survey remains of uncertain type. We present a new deep neural network approach for the classification of unidentified or…
The identification of low-energy counterparts for gamma-ray sources is one of the biggest challenge in modern gamma-ray astronomy. Recently, we developed and successfully applied a new association method to recognize gamma-ray blazar…
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…
We aim to test if a blazar candidate of uncertain-type (BCU) in the third Fermi active galactic nuclei catalog (3LAC) can be potentially classified as a BL Lac object or a flat spectrum radio quasar (FSRQ) by performing a statistical…