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Register a new userHunting misaligned radio-loud AGN (MAGN) candidates among the uncertain $gamma$-ray sources of the third Fermi-LAT Catalogue
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BL Lac Objects (BL Lacs) and Flat Spectrum Radio Quasars (FSRQs) are radio-loud active galaxies (AGNs) whose jets are seen at a small viewing angle (blazars), while Misaligned Active Galactic Nuclei (MAGNs) are mainly radiogalaxies of type FRI or FRII and Steep Spectrum Radio Quasars (SSRQs), which show jets of radiation oriented away from the observers line of sight. MAGNs are very numerous and well studied in the lower energies of the electromagnetic spectrum but are not commonly observed in the gamma-ray energy range, because their inclination leads to the loss of relativistic boosting of the jet emission. The Large Area Telescope (LAT) on board the Fermi Gamma-ray Space Telescope in the 100 MeV -300 GeV energy range detected only 18 MAGNs (15 radio galaxies and 3 SSRQs) compared to 1144 blazars. Studying MAGNs and their environment in the gamma-ray sky is extremely interesting, because FRI and FRII radio galaxies are respectively considered the parent populations of BL Lacs and FSRQs, and these account for more than 50% of the known gamma-ray sources. The aim of this study is to hunt new gamma-ray MAGN candidates among the remaining blazars of uncertain type and unassociated AGNs, using machine learning techniques and other physical constraints when strict classifications are not available. We found 10 new MAGN candidates associated with gamma-ray sources. Their features are consistent with a source with a misaligned jet of radiation. This study reinforces the need for more systematic investigation of MAGNs in order to improve understanding of the radiation emission mechanisms and and the disparity of detection between more powerful and weaker gamma-ray AGNs.
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We discuss the time-series behavior of 8 extragalactic 3FGL sources away from the Galactic plane (i.e., $mid bmid geq 10^{circ}$) whose uncertainty ellipse contains a single X-ray and one radio source. The analysis was done using the standard Fermi textit{ScienceTools}, package of version v10r0p5. The results show that sources in the study sample display a slight indication of flux variability in $gamma$-ray on monthly timescale. Furthermore, based on the object location on the variability index versus spectral index diagram, the positions of 4 objects in the sample were found to fall in the region of the already known BL Lac positions.
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Since 2008 August the Fermi Large Area Telescope (LAT) has provided continuous coverage of the gamma-ray sky yielding more than 5000 gamma-ray sources, but 54% of the detected sources remain with no certain or unknown association with a low energy counterpart. Rigorous determination of class type for a gamma-ray source requires the optical spectrum of the correct counterpart but optical observations are demanding and time-consuming, then machine learning techniques can be a powerful alternative for screening and ranking. We use machine learning techniques to select blazar candidates among uncertain sources characterized by gamma-ray properties very similar to those of Active Galactic Nuclei. Consequently, the percentage of sources of uncertain type drops from 54% to less than 12% predicting a new zoo for the Fermi gamma-ray sources. The result of this study opens up new considerations on the population of the gamma energy sky, and it will facilitate the planning of significant samples for rigorous analysis and multi-wavelength observational campaigns.
Radio-loud sources with blazar-like properties, but having a jet that does not directly point in the direction of the observer are among the most interesting classes of gamma-ray emitters. These sources are known as Misaligned Active Galactic Nuclei (MAGN). Understanding MAGN properties is useful to improve the knowledge of blazar energetics. We searched for new MAGN candidates among the remaining blazars of uncertain type detected by the Fermi Large Area Telescope (LAT) using a methodology based on characterizing their radio morphology. We identified seven new candidates associated with gamma-ray sources. Their features are consistent with a source with a misaligned relativistic jet consistent with the definition of MAGN.
Blazars and in particular the subclass of high synchrotron peaked Active Galactic Nuclei are among the main targets for the present generation of Imaging Atmospheric Cherenkov Telescopes (IACTs) and will remain of great importance for very high-energy $gamma$-ray science in the era of the Cherenkov Telescope Array (CTA). Observations by IACTs, which have relatively small fields of view ($sim$ few degrees), are limited by viewing conditions; therefore, it is important to select the most promising targets in order to increase the number of detections. The aim of this paper is to search for unclassified blazars among known $gamma$-ray sources from the Fermi Large Area Telescope (LAT) third source catalog that are likely detectable with IACTs or CTA. We use an artificial neural network algorithm and updated analysis of Fermi-LAT data. We found 80 $gamma$-ray source candidates, and for the highest-confidence candidates, we calculate their potential detectability with IACTs and CTA based on an extrapolation of their energy spectra. Follow-up observations of our source candidates could significantly increase the current TeV source population sample and could ultimately confirm the efficiency of our algorithm to select TeV sources.
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