Unsupervised classification of the intrinsic and morphological properties of quasars through self‐organizing maps. Issue 4 (28th April 2020)

• Record Type: Journal Article
• Authors: Karafistan, Aysel; Gemikonakli, Eser
• Is Part Of: Astronomische Nachrichten. Volume 341:Issue 4(2020)
• Page Start: 471
• Page End: 477
• Publication Date: 2020-04-28
• Subjects: quasars -- luminosity and redshift -- morphology -- classification -- neural network; Astronomy -- Periodicals; 520.5
• DOI: 10.1002/asna.202013681
• Languages: English
• ISSNs: 0004-6337

Abstract

Abstract: 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.