Effective spectral index properties for Fermi blazars

Yang, Jiang-He; Fan, J. H.; Liu, Yi; Zhang, Yuelian; Man-xian, Tuo; Nie, Jianjun; Yuan, Yu-Hai

doi:10.1007/s11433-017-9136-7

“…However, there are 256 uncertain type BCUs forecast. The different physical origins of γ-ray emissions in BL Lacs and FSRQs may account for their distinct gammaray properties (e.g., Bhattacharya et al 2016;Fan et al 2016b;Yang et al 2018;Boula et al 2019). For BL Lacs, the γ-ray emission is generally believed to originate from a pure synchrotron self-Compton (SSC) process (e.g., Mastichiadis & Kirk 1997;Krawczynski et al 2004;Zhang et al 2014), while for FSRQs, the γ-ray emission is generally believed to originate from the SSC + EC (external-Compton) radiation process (e.g., Sambruna et al 1999;Böttcher & Chiang 2002;Yan et al 2014;Ghisellini et al 2011;Chen & Bai 2011;Zheng et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning

Kang¹,

Li²,

Ou³

et al. 2019

ApJ

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The recently published fourth Fermi Large Area Telescope source catalog (4FGL) a)b) , reports 5065 gamma-ray sources in terms of direct observational gamma-ray properties. Among the sources, the largest population is the Active Galactic Nuclei (AGN), which consists of 3137 blazars, 42 radio galaxies, and 28 other AGNs. The blazar sample comprises 694 flat-spectrum radio quasars (FSRQs), 1131 BL Lac-type objects (BL Lacs), and 1312 blazar candidates of an unknown type (BCUs). The classification of blazars is difficult using optical spectroscopy given the limited knowledge with respect to their intrinsic properties, and the limited availability of astronomical observations. To overcome these challenges, machine learning algorithms are being investigated as alternative approaches. Using the 4FGL catalog, a sample of 3137 Fermi blazars with 23 parameters is systematically selected. Three established supervised machine learning algorithms (random forests (RFs), support vector machines (SVMs), artificial neural networks (ANNs)) are employed to general predictive models to classify the BCUs. We analyze the results for all of the different combinations of parameters. Interestingly, a previously reported trend the use of more parameters leading to higher accuracy is not found. Considering the least number of parameters used, combinations of eight, 12 or 10 parameters in the SVM, ANN, or RF generated models achieve the highest accuracy (Accuracy ≃ 91.8%, or ≃ 92.9%). Using the combined classification results from the optimal combinations of parameters, 724 BL Lac type candidates and 332 FSRQ type candidates are predicted; however, 256 remain without a clear prediction.

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“…Blazars are exciting subclass of active galactic nuclei (AGNs) that are thought to be very bright and inconsistent sources of high energy (Abdo et al 2009(Abdo et al , 2010aYefimov 2011). They reportedly have other outstanding observational features like large and variable luminosity, continuous spectral energy distributions (SEDs), apparent superluminal motions, high and variable polarization and very active GeV to TeV γ-ray emissions (Andruchow et al 2005;Abdo et al 2010b;Yang et al 2018). The strong non-thermal continuum emissions from these objects cover the whole electromagnetic spectrum from a relativistic jet observed along the line of sight, thus, giving rise to relativistic magnification (Urry and Padovani 1995;Blandford and Rees 1978).…”

Section: Introductionmentioning

confidence: 99%

Spectral Properties of Fermi Blazars and their Unification Schemes

Iyida

¹

,

Odo

²

,

Chukwude

³

et al. 2020

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We use the distributions of spectral indices (αv ) of a large homogenous sample of Fermi-detected blazars to re-investigate the relationship between flat spectrum radio quasars (FSRQs) and subclasses of BL Lac objects (BL Lacs). We compute the broadband synchrotron and Compton spectral indices from radio-to-X-ray and X-ray to y-ray bands, respectively. Analyses of our data show continuity in the distributions of the spectral indices from FSRQs to HSP through LSP and ISP subclasses of BL Lacs. We find from y-ray luminosity distribution that the jetted radio galaxies form the low-luminosity tail of the distribution, which is suggestive that the sequence can be extended to the young jetted galaxy populations. We observe a significant difference in the shape of Compton and synchrotron spectra: significant anti-correlation (r ∼−0.80) exists between the broadband Compton and synchrotron spectral indices. Furthermore, the broadband spectral indices vary significantly with redshift (z) at low redshift (z < 0.3) and remain fairly constant at high (z ≥ 0.3) redshift. The trend of the variations suggests a form of evolutionary connection between subclasses of blazars. Thus, while selection effect may be significant at low redshift, evolutionary sequence can also be important. Our results are not only consistent with a unified scheme for blazars and their young jetted galaxy counterparts but also suggest that the broadband spectral sequence of blazars is not a secondary effect of redshift dependence.

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“…(3) α RO and α OX . The effective spectrum index from frequency ν 1 to frequency ν 2 is defined (Ledden & Odell 1985;Fan et al 2012;Yang et al 2018;Nie et al 2020) as a =n n…”

Section: Correlationsmentioning

confidence: 99%

The Spectral Energy Distributions for 4FGL Blazars

Yang¹,

Fan²,

Liu³

et al. 2022

ApJS

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In this paper, the multiwavelength data from radio to X-ray bands for 2709 blazars in the 4FGL-DR3 catalog are compiled to calculate their spectral energy distributions using a parabolic equation log ( ν f ν ) = P 1 log ν − P 2 2 + P 3 . Some important parameters including spectral curvature (P 1), synchrotron peak frequency (P 2, log ν p ), and peak luminosity ( log L p ) are obtained. Based on those parameters, we discussed the classification of blazars using the “Bayesian classification” and investigated some mutual correlations. We came to the following results. (1) Based on the Bayesian classification of synchrotron peak frequencies, the 2709 blazars can be classified into three subclasses, i.e., log ( ν p / Hz ) < 13.7 for low synchrotron peak blazars (LSPs), 13.7 < log ( ν p / Hz ) < 14.9 for intermediate synchrotron peak blazars (ISPs), and log ( ν p / Hz ) > 14.9 for high synchrotron peak blazars (HSPs), and there are 820 HSPs, 750 ISPs, and 1139 LSPs. (2) The γ-ray emission has the closest relationship with radio emission, followed by optical emission, while the weakest relationship is that with X-ray emission. The γ-ray luminosity is also correlated with the synchrotron peak luminosity. (3) There are strong positive correlations between the curvature (1/∣P 1∣) and the peak frequency ( log ν p ) for all subclasses (FSRQs, (high, intermediate, and low) BL Lacertae objects). For different subclasses, the correlation slopes are different, which implies that there are different acceleration mechanisms and emission processes for different subclasses of blazars.

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Effective spectral index properties for Fermi blazars

Cited by 11 publications

References 29 publications

Evaluating the Classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning

Evaluating the Classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning

Spectral Properties of Fermi Blazars and their Unification Schemes

The Spectral Energy Distributions for 4FGL Blazars

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