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Unsupervised classification of the intrinsic and morphological properties of quasars through self‐organizing maps
Author(s) -
Karafistan Aysel,
Gemikonakli Eser
Publication year - 2020
Publication title -
astronomische nachrichten
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 63
eISSN - 1521-3994
pISSN - 0004-6337
DOI - 10.1002/asna.202013681
Subject(s) - quasar , astrophysics , physics , principal component analysis , redshift , luminosity , cluster analysis , galaxy , curse of dimensionality , radio galaxy , self organizing map , pattern recognition (psychology) , artificial intelligence , computer science
The aim of this paper is to unveil, by means of self‐organizing maps (SOMs), the connections between intrinsic properties of radio quasars, namely redshift ( z ), luminosity ( P ), continuum slope ( α ) , and parameters characterizing radio morphology of the host galaxy: observed linear size and bending. The strong luminosity–redshift correlation established by a prior traditional principal component analysis (PCA) is confirmed for the same dataset that contains 36 nearby ( z < 1.5) and 44 distant ( z > 1.5) quasars with five parameters. The SOM visualizations disentangled the luminosity–linear size anti‐correlation, supporting a dimensionality of p = 2 for the quasar parameter space. The SOM also clustered radio quasars into subgroups with common physical properties. We expect this work to provide a new probe, leading to further clustering of nonlabeled data with respect to the evolution of morphological properties.