IX/60             Fermi superluminal sources                       (Xiao+, 2020)
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Beginning of ReadMe : IX/60 Fermi superluminal sources (Xiao+, 2020) ================================================================================ Comparison between Fermi detected and non-Fermi detected superluminal sources Xiao H.B., Fan J.H., Yang J.H., Liu Y., Yuan Y.H., Tao J., Costantin D., Zhang Y.T., Pei Z.Y., Zhang L.X., Yang W.X. <Science China Physics, Mechanics & Astronomy, 62, 129811 (2019)> Further evidence of superluminal active galactic nuclei as gamma-ray sources Xiao J.H., Fan R.. Rando J.T.. Zhu H.B., Hu L.J. <Astron. Nachrichten, 341, 462-470 (2020)> Efficient Fermi source identification with machine learning methods Xiao H.B., Cao H.T., Fan J.H., Costantin D., Luo G.Y., Pei Z.Y. <Astronomy and Computing, 32, 100387 (2020)> =2020yCat.9060....0X =2019SCPMA..62l9811X +2020AN....341..462X +2020A&C....3200387X ================================================================================ ADC_Keywords: Gamma rays - Active gal. nuclei Keywords: active galactic nuclei - jets - gamma-rays - correlations - 4FGL - superluminal motion - Fermi source - blazar - dimensionality reduction - ensemble method - grid search Abstract: Paper I (2019SCPMA..62l9811X): Active galactic nuclei (AGNs) have been attracting research attention due to their special observable properties. Specifically, a majority of AGNs are detected by Fermi-LAT missions, but not by Fermi-LAT, which raises the question of weather any differences exist between the two. To answer this issue, we compile a sample of 291 superluminal AGNs (189 FDSs and 102 non-FDSs) from available multi-wavelength radio, optical, and X-ray (or even gamma-ray) data and Doppler factors and proper motion (mu) (or apparent velocity (betaapp)); calculated the apparent velocity from their proper motion, Lorentz factor (GAMMA), viewing angle (phi) and co-moving viewing angle (phico) for the sources with available Doppler factor (delta); and performed some statistical analyses for both types. Our study indicated that (1) in terms of average values, FDSs have higher proper motions (mu), apparent velocities (beta_app_), Doppler factor (delta), Lorentz factor (GAMMA), and smaller viewing angle (phi). Nevertheless, there is no clear difference in co-moving viewing angles (phi_co_). The results reveal that FDSs show stronger beaming effect than non-FDSs. (2) In terms of correlations: 1) both sources show positive, mutually correlated fluxes, which become closer in de-beamed fluxes; 2) with respect to apparent velocities and gamma-ray luminosity, there is a tendency for the brighter sources to have higher velocities; 3) with regard to viewing angle and observed gamma-ray luminosity, logphi=-(0.23+/-0.04)logLgamma+(11.14+/-1.93), while for the co-moving viewing angle and the intrinsic gamma-ray luminosity, logphi_co_=(0.09+/-0.01)logL^in^_(gamma)_-(1.73+/-0.48). These correlations show that the luminous gamma-ray sources have smaller viewing angles and a larger co-moving viewing angle, which indicate a stronger beaming effect in gamma-ray emissions. Paper II (2020AN....341..462X): In our previous work in Xiao et al. (SCPMA, 2019, 62, 129811), we suggested that six superluminal sources could be gamma-ray candidates, and in fact, five of them have been confirmed in the fourth Fermi-LAT source catalog (4FGL). In this work, based on the 4FGL, we report a sample of 229 Fermi detected superluminal sources (FDSs), including 40 new FDSs and 62 non-FDSs. Thus, we believe that all superluminal sources should have gamma-ray emissions, and superluminal motion could also be a clue to detect gamma-ray emission from active galactic nuclei. We present a new approach of Doppler factor estimate through the study of the gamma-ray luminosity (Lgamma) and of the viewing angle (phi). Paper III (2020A&C....3200387X): In this work, Machine Learning (ML) methods are used to efficiently identify the unassociated sources and the Blazar Candidate of Uncertain types (BCUs) in the Fermi-LAT Third Source Catalog (3FGL). The aims are twofold: (1) to distinguish the Active Galactic Nuclei (AGNs) from others (non-AGNs) in the unassociated sources; (2) to identify BCUs into BL Lacertae objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs). Two dimensional reduction methods are presented to decrease computational complexity, where Random Forest (RF), Multilayer Perceptron (MLP) and Generative Adversarial Nets (GAN) are trained as individual models. In order to achieve better performance, the ensemble technique is further explored. It is also demonstrated that grid search method is of help to choose the hyperparameters of models and decide the final predictor, by which we have identified 748 AGNs out of 1010 unassociated sources, with an accuracy of 97.04%. Within the 573 BCUs, 326 have been identified as BL Lacs and 247 as FSRQs, with an accuracy of 92.13%. Description: Paper I: From the available literature, we compile 291 sources with superluminal motions, including 189 (142 FSRQs, 39 BL Lacs, 5 galaxies and 2 uncertain type blazar candidates (BCU) and 1 unknown type of AGN without a known red- shift) Fermi detected superluminal sources (FDS) and 102 (98 FSRQs, 1 BL Lac and 12 galaxies and 1 unknown type of AGN without a known redshift) non-Fermi detected superluminal (non-FDS) sources, where Fermi detected sources are detected by Ferni-LAT telescope and listed in the Fermi AGB catalogues. There are 816 components for the 189 FDS sources in total. 30 of them have just one component. In the present sample, we also include the gamma-ray emission source, 0007+106 (III ZW2), which was classified as an gammaray source by Liao et al. (2016, Cat. J/ApJS/226/17). All the FDS sources are listed in Table 1. For the 102 non-FDS sources (88 FSRQs, 1 BL Lac, 12 galaxies and 1 unknown type of AGN) with 400 components totally, 17 of them have just one component, they are in Table 2. Paper II: We matched the sources in our non-FDSs sample with the sources in the 4FGL catalog and found that 40 of 102 sources that were non-FDSs in 3FGL are FDSs in 4FGL. They are listed in Table 1. Hence, we have an updated sample of 229 FDSs from 3FGL and 4FGL, with 62 non-FDSs remaining. Paper III: Machine Learning methods have proven to be a promising approach to process astronomical data and they provide classification based on high-dimensionality patterns that human investigation may miss in the first place. In this paper, we used ML methods to complete the source identification in 3FGL for further astrophysical studies. The FS and PCA techniques indeed helped to develop a more efficient algorithm, and ensemble models performed better on unseen samples. Grid search method was demonstrated to be of help to choose the hyper-parameters. With these ML methods, we have successfully identified 748 AGNs out of 1010 unassociated sources, with an accuracy of 97.04%. Within 573 BCUs, 326 have been identified as BL Lacs and 247 as FSRQs, with an accuracy of 92.13%.

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