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 2020yCat.9060....0X
=2019SCPMA..62l9811X 2019SCPMA..62l9811X
+2020AN....341..462X 2020AN....341..462X
+2020A&C....3200387X 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 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 γ-ray) data and Doppler
factors and proper motion (µ) (or apparent velocity (βapp));
calculated the apparent velocity from their proper motion, Lorentz
factor ({GAMMA}), viewing angle (φ) and co-moving viewing angle
(φco) for the sources with available Doppler factor (δ); and
performed some statistical analyses for both types. Our study
indicated that (1) in terms of average values, FDSs have higher proper
motions (µ), apparent velocities (βapp), Doppler factor
(δ), Lorentz factor ({GAMMA}), and smaller viewing angle
(φ). Nevertheless, there is no clear difference in co-moving
viewing angles (φco). 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
γ-ray luminosity, there is a tendency for the brighter sources
to have higher velocities; 3) with regard to viewing angle and
observed γ-ray luminosity,
logφ=-(0.23±0.04)logLγ+(11.14±1.93), while for the
co-moving viewing angle and the intrinsic γ-ray luminosity,
logφco=(0.09±0.01)logLin(gamma)-(1.73±0.48). These
correlations show that the luminous γ-ray sources have smaller
viewing angles and a larger co-moving viewing angle, which indicate a
stronger beaming effect in γ-ray emissions.
Paper II (2020AN....341..462X 2020AN....341..462X):
In our previous work in Xiao et al. (SCPMA, 2019, 62, 129811), we
suggested that six superluminal sources could be γ-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 γ-ray emissions, and
superluminal motion could also be a clue to detect γ-ray
emission from active galactic nuclei. We present a new approach of
Doppler factor estimate through the study of the γ-ray
luminosity (Lγ) and of the viewing angle (φ).
Paper III (2020A&C....3200387X 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
γ-ray emission source, 0007+106 (III ZW2), which was classified
as an γray 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%.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
cat1-1.dat 127 816 Superluminal sources detected by Fermi-LAT
(data for 377 individual sources)
(Paper I, table 1)
cat1-2.dat 103 383 Superluminal sources not detected by Fermi-LAT
(Paper I, table 2)
cat2-1.dat 73 40 New Fermi detected superluminal sources from
4FGL (table1, Paper II) (Paper II, table 1)
cat2-3.dat 88 181 181 Fermi detected superluminal sources with
available Doppler factor (Paper II, table 3)
cat2-5.dat 56 1505 1505 blazars' luminosity-Doppler factor
(Paper II, table 5)
cat2-6.dat 63 187 187 Fermi detected superluminal sources with
total and component flux from Lister et al.
(2018, Cat. J/ApJS/234/12) and Lister et al.
(2019, Cat. J/AJ/138/1874) (Paper II, table 6)
cat3-7.dat 59 1010 Identification results of the 1010 unassociated
sources with Best Model A (Paper III, table 7)
cat3-8.dat 59 573 Identification results of the 573 BCUs with
Best Model B ((Paper III, table 8)
cat3-15.dat 35 113 113 uncertain BCUs from Chiaro et al. (2016,
Cat. J/MNRAS/462/3180) and Lefaucheur and
Pita (2017, Cat. J/A+A/602/A86)
(Paper III, table 15)
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See also:
J/ApJ/715/429 : First Fermi-LAT AGN catalog (1LAC) (Abdo+, 2010)
J/ApJ/743/171 : The 2LAC catalog (Ackermann+, 2011)
J/ApJS/188/405 : Fermi-LAT first source catalog (1FGL) (Abdo+, 2010)
J/ApJS/199/31 : Fermi LAT second source catalog (2FGL) (Nolan+, 2012)
J/ApJS/218/23 : Fermi LAT third source catalog (3FGL) (Acero+, 2015)
J/ApJS/247/33 : The Fermi LAT fourth source catalog (4FGL) (Abdollahi+, 2020)
J/AJ/138/1874 : MOJAVE VI. Kinematic analysis of blazar jets (Lister+, 2009)
J/ApJS/234/12 : MOJAVE XV. AGN jets VLBA 15GHz obs. 1996-2016 (Lister+ 2018)
J/MNRAS/462/3180 : 3FGL Blazar of Unknown Type classification (Chiaro+, 2016)
J/A+A/602/A86 : Blazar cand. among Fermi/LAT 3FGL catalog (Lefaucheur+ 2017)
Byte-by-byte Description of file: cat1-1.dat
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Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 18 A18 --- Name Source name
20- 24 A5 --- Class Classification (G1)
26- 33 F8.6 --- z ? Redshift
35- 39 F5.2 --- delta ? Doppler factor
40- 43 A4 --- r_delta Reference for deltaR (G2)
45- 48 F4.1 mag Rmag ? R magnitude from BZCAT
50- 55 F6.3 mag Ext ? Galactic extinction, AR, from NED
57- 61 I5 --- F1.4GHz ? Flux density at 1.4GHz from BZCAT
63- 68 F6.2 --- FX ? X-ray flux in the 0.1-2.4keV band
from BZCAT
70- 75 F6.4 --- alphaph ? gamma-ray photom spectral index
77- 84 E8.3 --- Flux1000 ? gamma-ray photon flux in 0.1-100Gev range
85 A1 --- n_Flux1000 [F] Note on Flux1000
87- 94 F8.3 uas/yr pm ? Proper motion
95-100 F6.2 uas/yr e_pm ? rms uncertainty on pm
101 A1 --- n_pm [e] Note on pm
103-105 A3 --- Comp Component
107-112 A6 --- r_pm Proper motion reference (G3)
114-119 F6.3 --- beta ? Apparent velocity
121-127 F7.4 --- e_beta ? rms uncertainty on beta
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Byte-by-byte Description of file: cat1-2.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 10 A10 --- Name Name (HHMM+DDd)
12- 17 A6 --- Class Classification (G1)
19- 26 F8.6 --- z ? Redshift
28- 32 F5.2 --- delta ? Doppler factor
34- 37 A4 --- r_delta Reference for deltaR (G2)
39- 42 F4.1 mag Rmag ? R magnitude from BZCAT
44- 48 F5.3 mag Ext ? Calactic exteinction, AR, from NED
50- 54 I5 --- F1.4GHz ? Flux density at 1.4GHz from BZCAT
(1.4/0.843GHz)
56- 60 F5.2 --- FX ? X-ray flux in the 0.1-2.4keV band from
BZCAT
62- 68 F7.2 uas/yr pm ? Proper motion
70- 75 F6.2 uas/yr e_pm ? rms uncertainty on pm
76 A1 --- n_pm [e] Note on pm
78- 80 A3 --- Comp Component
82- 89 A8 --- r_pm Proper motion reference (G3)
91- 96 F6.3 --- beta ? Apparent velocity
98-103 F6.3 --- e_beta ? rms uncertainty on beta
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Byte-by-byte Description of file: cat2-1.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 17 A17 --- Name Source name (4FGL JHHMM.m+DDMM)
19- 26 A8 --- OName Other name (HHMM+DDd)
28- 32 A5 --- Class Classification (G1)
34- 38 F5.3 --- z Redshift
40- 44 F5.2 --- deltaR ? Radio Doppler factor
46- 48 A3 ---- r_deltaR Reference for deltaR (G2)
50- 57 E8.3 ph/cm2/s Flux1000 Integral photon flux from 1 to 100GeV
59- 66 E8.3 ph/cm2/s e_Flux1000 rms uncertainty on integral photon flux
from 1 to 100GeV
68- 73 F6.4 --- alphaph gamma-ray photon spectral index
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Byte-by-byte Description of file: cat2-3.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 17 A17 --- Name Source name
(3FGL JHHMM.m+DDMM or 4FGl JHHMM.m+DDMM)
19- 31 A13 --- OName Other name
33- 37 A5 --- Class Classification (G1)
40- 44 F5.3 --- z Redshift
48- 52 F5.2 --- deltaR ? Radio Doppler factor
54- 56 A3 ---- r_deltaR Reference for deltaR (G2)
58- 65 E8.3 ph/cm2/s Flux1000 Integral photon flux from 1 to 100GeV
67- 72 F6.4 --- alphaph gamma-ray photon spectral index
75- 80 F6.3 --- beta Maximun apparent velocity, betamax*appc
82- 88 F7.3 --- deltaLg Estimated Doppler factor of this work
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Byte-by-byte Description of file: cat2-5.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 17 A17 --- Name Source name (4FGl JHHMM.m+DDMM)
19- 21 A3 --- Class Classification (G1)
23- 30 F8.6 --- z Redshift
32- 41 E10.5 ph/cm2/s Flux1000 Integral photon flux from 1 to 100GeV
43- 48 F6.4 --- alphaph gamma-ray photon spectral index
51- 56 F6.2 --- deltaLg Estimated Doppler factor
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Byte-by-byte Description of file: cat2-6.dat
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Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 17 A17 --- Name Source name
(3FGL JHHMM.m+DDMM or 4FGl JHHMM.m+DDMM)
19- 23 A5 --- Class Classification (G1)
25- 29 F5.3 --- z Redshift
31- 33 A3 --- Comp Component id
34- 37 I4 mJy IntF15GHzcomp Component flux density at 15GHz
39- 46 F8.2 mJy IntF15GHztot Total flux density at 15GHz
48- 53 F6.4 --- alphaph gamma-ray photon spectral index
56- 63 E8.3 ph/cm2/s Flux1000 Integral photon flux from 1 to 100GeV
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Byte-by-byte Description of file: cat3-7.dat cat3-8.dat
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Bytes Format Units Label Explanations
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1- 17 A17 --- Name Source name (3FGL JHHMM.m+DDMM)
18 A1 --- n_Name [c] Note on Name (1)
20- 25 F6.2 deg GLON Galactic longitude
27- 32 F6.2 deg GLAT Galactic latitude
34- 38 F5.3 --- SpIndex Spectral Index
40- 47 F8.3 --- VarIndex Variability Index
49- 51 A3 --- TypePred Prediction type (2)
53- 59 F7.5 --- Like Likelihood
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Note (1): c indicates that based on the region of the sky the source is is
considered to be potentially confused with Galactic diffuse emission.
Note (2): Prediction type: AGN or Non for cat3-7.dat, B or F for cat3-8.dat.
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Byte-by-byte Description of file: cat3-15.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 17 A17 --- Name Source name (3FGL JHHMM.m+DDMM)
19- 21 A3 --- C2016 [Unc ] Uncertain BCUs from Chiaro et al.
(2016, Cat. J/MNRAS/462/3180)
23- 25 A3 --- L2017 [Unc ] Uncertain BCUs from Lefaucheur and Pita
(2017, Cat. J/A+A/602/A86)
27 A1 --- TW [FB] Prediction from this work
29- 35 F7.5 --- Like Likelihood
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Global notes:
Note (G1): Classification as follows:
F = FSRQs
B = BL Lacs
Sy = Seyfert galaxy
Sy1.2 = Seyfert galaxy 1.2
Sy1.5 = Seyfert galaxy 1.5
G = galaxy
BCU = blazar candidates of uncertain type
QSO = quasar
Un = uncertain
Note (G2): Reference for deltaR as follows:
H09 = Hovatta et al. (2009, Cat. J/A+A/496/527)
L18 = Liodakis et al. (2018, Cat. J/ApJS/234/12)
Note (G3): References as follows:
B08 = Britzen et al. (2008, Cat. J/A+A/484/119)
F09 = Fan et al. (2009PASJ...61..639F 2009PASJ...61..639F)
GPC99 = Gabuzda et al. (1999MNRAS.307..725G 1999MNRAS.307..725G)
H09 = Hovatta et al. (2009A&A...494..527H 2009A&A...494..527H)
JM01 = Jorstad et al. (2001ApJS..134..181J 2001ApJS..134..181J)
L18 = Liodakis et al. (2018, Cat. J/ApJ/866/137)
PBE06 = Piner et al. (2006ApJ...640..196P 2006ApJ...640..196P)
S10 = Savolainen et al. (2010A&A...512A..24S 2010A&A...512A..24S)
VC94 = Vermeulen & Cohen (1994, Cat. J/ApJ/430/467)
X95 = Xu et al. (1995, Cat. J/ApJS/99/297)
Frey2014 = Frey et al. (2015MNRAS.446.2921F 2015MNRAS.446.2921F)
Gio99a = Giovannini et al. (1999ApJ...522..101G 1999ApJ...522..101G)
Marc85 = Marcaide et al. (1985A&A...142...71M 1985A&A...142...71M)
SHW98 = Shen et al. (1998ChA&A..22..133S 1998ChA&A..22..133S)
CJF = Caltech-Jodrel Bank Flat-spectrum sample:
http://www.mpifr-bonn.mpg.de/staff/sbritzen/cjf.html
MOJAVE = Monitoring Of Jet in Active galactic nuclei with VLBA Experiments:
http://www.physics.purdue.edu/MOJAVE
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Acknowledgements:
Hubing Xiao, hubing.xiao(at)outlook.com
(End) Patricia Vannier [CDS] 15-Jul-2020