Radio variable sources at 1400 MHz and their optical variability

Abrahamyan, H. V.; Mickaelian, A. M.; Paronyan, G. M.; Mikayelyan, G. A.; Gyulzadyan, M. V.

doi:10.1016/j.ascom.2018.09.012

Cited by 12 publications

(12 citation statements)

References 18 publications

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“…In our recent papers (Abrahamyan et al, 2018a;Abrahamyan et al, 2018b;Mikayelyan et al, 2018;Paronyan et al, 2018;Abrahamyan et al, 2019a;Paronyan et al, 2019) we have introduced and developed fine classification of active galaxies to have better understanding of the detailed spectral features between the subclasses. In this paper we use objects provided by Abrahamyan et al (2018b), where 6,301 sources are listed extracted from the comparison of NRAO VLA Sky Survey (NVSS) (Condon et al, 1998) and Faint Images of the Radio Sky at Twenty cm (FIRST) (Helfand et al, 2015) radio catalogues. All of them are characterized by radio variability, and 25.5% of them also show optical variability.…”

Section: The Sample Of Studied Objectsmentioning

confidence: 99%

Fine Classification of QSOs and Seyferts for Activity Types Based on SDSS Spectroscopy

Mickaelian

Abrahamyan

Paronyan

et al. 2021

Front. Astron. Space Sci.

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Using the SDSS spectroscopy, we have carried out fine optical spectral classification for activity types for 710 AGN candidates. These objects come from a larger sample of some 2,500 candidate AGN using pre-selection by various samples; bright objects of the Catalog of Quasars and Active Galactic Nuclei, AGN candidates among X-ray sources, optically variable radio sources, IRAS extragalactic objects, etc. A number of papers have been published with the results of this spectral classification. More than 800 QSOs have been identified and classified, including 710 QSOs, Seyferts and Composites. The fine classification shows that many QSOs show the same features as Seyferts, i.e., subtypes between S1 and S2 (S1.2, S1.5, S1.8 and S1.9). We have introduced subtypes for the QSOs: QSO1.2, QSO1.5, QSO1.8, QSO1.9, though the last subtype does not appear in SDSS wavelength range due to mostly highly redshifted Hα (the main line for identification of the 1.9 subtype). Thus, independent of the luminosity (which serves as a separator between QSOs and Seyferts), AGN show the same features. We also have classified many objects as Composites, spectra having composite characteristics between Sy and LINERs, Sy and HII or LINERs and HII; in some cases all three characteristics appear together resulting as Sy/LINER/HII subtype. The QSOs subtypes together with Seyfert ones allow to follow AGN properties along larger redshift range expanding our knowledge on the evolution of AGN to more distant Universe represented by QSOs.

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Section: The Sample Of Studied Objectsmentioning

confidence: 99%

Fine Classification of QSOs and Seyferts for Activity Types Based on SDSS Spectroscopy

Mickaelian

Abrahamyan

Paronyan

et al. 2021

Front. Astron. Space Sci.

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“…To get rid of systematic effects of photometric measurements, we counted the systematic shifts between these catalogs (Abrahamyan et al ). In the next step, we counted average magnitudes in POSS1 and POSS2 both for red and blue.…”

Section: Study Of the Optical Variabilitymentioning

confidence: 99%

“…Having the numbers from Table , we calculated the best magnitude for each sources (blue and red) using the statistical weighted method. The idea is that each catalog measurement contributes to calculating the best magnitude according to its accuracy, so that the best magnitude is derived by all possible measurement contributions (Abrahamyan et al ). The method is similar to the one introduced by Mickaelian & Sinamyan () for the positions and by Mickaelian et al () for the magnitudes.…”

Section: Study Of the Optical Variabilitymentioning

confidence: 99%

“…Using the same method, we estimated errors for each “best magnitude” (“best errors”). And having the best errors, we counted sigma differences for B and R magnitudes in POSS1 and POSS2 (Abrahamyan et al ):

σ_{normalB} = \sqrt{k_{1}^{2} + k_{2}^{2}},

σ_{normalR} = \sqrt{t_{1}^{2} + t_{2}^{2}},

where k 1 and t 1 are the best errors for B and R magnitudes in POSS1, k 2 and t 2 are best errors for B and R magnitudes in POSS2. For the B magnitude, the average σ B = 0.48 and for R, the average σ R = 0.38.…”

Section: Study Of the Optical Variabilitymentioning

confidence: 99%

“…Thus, we have 2,121 radio sources that have optical variability. For each source, we have four means for understanding their variability and for each source, based on this, we give variability category flags from 0 to 3 (Abrahamyan et al ). For a detailed picture of variability of these sources, we counted 4 category flags together (Table ).…”

Section: Study Of the Optical Variabilitymentioning

confidence: 99%

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Optical variability of blazars

et al. 2019

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The analysis of blazars' parameters from BZCAT leads to a conclusion that they do not have the same properties. The preliminary criterion to include an object in the catalog was the strong radio emission; however, two type of radio sources were selected: BL Lacertae (BLL) objects and Flat Spectrum Radio Quasars (FSRQ). As a number of properties are typical of blazars (strong radio emission, optical variability, continuum optical spectra, polarization, high luminosity, etc.), using the optical data, we investigate them to clarify which property plays the most significant role in their classification as blazars. We found that 60% of blazars have optical variability. We use a technique developed based on POSS1 and POSS2 photometry and group the variability into extreme, strong, medium, and low classes. In the optical range, 51 blazars have powerful variability (extreme variables), and 126 are high variables. In addition, 63% of blazars have detected radiation in X‐ray and 28% have detected radiation in gamma rays. We give the average statistical characteristics of blazars based on our analysis and calculations.

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