J/MNRAS/470/1291 Classifying 3FGL with ANN (Salvetti+, 2017)
3FGLzoo: classifying 3FGL unassociated Fermi-LAT γ-ray sources by
artificial neural networks.
Salvetti D., Chiaro G., La Mura G., Thompson D.J.
<Mon. Not. R. Astron. Soc., 470, 1291-1297 (2017)>
=2017MNRAS.470.1291S 2017MNRAS.470.1291S (SIMBAD/NED BibCode)
ADC_Keywords: Gamma rays ; Active gal. nuclei ; BL Lac objects
Keywords: methods: statistical - galaxies: active -
BL Lacertae objects: general - gamma-rays: galaxies
Abstract:
In its first four years of operation, the Fermi-Large Area Telescope
(LAT) detected 3033 γ-ray emitting sources. In the Fermi-LAT
Third Source Catalogue (3FGL) about 50 per cent of the sources have no
clear association with a likely γ-ray emitter. We use an
artificial neural network algorithm aimed at distinguishing BL Lacs
from FSRQs to investigate the source subclass of 559 3FGL unassociated
sources characterized by γ-ray properties very similar to those
of active galactic nuclei. Based on our method, we can classify 271
objects as BL Lac candidates, 185 as FSRQ candidates, leaving only 103
without a clear classification. We suggest a new zoo for γ-ray
objects, where the percentage of sources of uncertain type drops from
52 per cent to less than 10 per cent. The result of this study opens
up new considerations on the population of the γ-ray sky, and it
will facilitate the planning of significant samples for rigorous
analyses and multiwavelength observational campaigns.
Description:
One of the main goals of our investigation is to complete the census
of blazar subclasses in the 3FGL source catalogue using the ANN
technique based on B-FlaP (Chiaro et al., 2016MNRAS.462.3180C 2016MNRAS.462.3180C,
Cat. J/MNRAS/462/3180).
We applied our algorithm to 559 3FGL unassociated sources classified
as likely AGNs to investigate their source subclass. These sources can
be divided in 271 BLL candidates, 185 FSRQ candidates, leaving only
103 without a clear classification.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table1.dat 60 559 *3FGL unassociated (UCS) source
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Note on table1.dat: classified as likely AGN in Saz Parkinson et al.
(2016ApJ...820....8S 2016ApJ...820....8S, Cat. J/ApJ/820/8) the likelihood membership class
relied on B-FlaP method (Chiaro et al., 2016MNRAS.462.3180C 2016MNRAS.462.3180C,
Cat. J/MNRAS/462/3180), which uses the artificial neural networks (ANN)
algorithm with the empirical cumulative distribution function of the
gamma-ray flux as key parameter.
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See also:
J/ApJ/820/8 : 3FGL sources statistical classifications
(Saz Parkinson+, 2016)
J/MNRAS/462/3180 : 3FGL Blazar of Unknown Type classification (Chiaro+, 2016)
Byte-by-byte Description of file: table1.dat
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Bytes Format Units Label Explanations
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1- 13 A13 --- Name 3FGL Name (JHHMM.m+DDMMa)
15- 20 F6.2 deg GLON Galactic longitude
22- 28 F7.3 deg GLAT Galactic longitude
30- 37 E8.3 --- LBLL [0/1] ANN likelihood to be classified as BL Lac
39- 46 E8.3 --- LFRRQ [0/1] ANN likelihood to be classified as FSRQac
48- 60 A13 --- Class Predicted classification (1)
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Note (1): Sources classified as BL Lac candidates if LBLL>0.566,
and classified as FSRQ if LFRRQ> 0.770
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History:
From electronic version of the journal
(End) Patricia Vannier [CDS] 23-Apr-2020