Clustering by reordering of similarity and Laplacian matrices: Application to galaxy clusters

Mahmoud, Eman A.; Shoukry, Amin; Takey, A.

doi:10.1016/j.ascom.2018.02.001

Cited by 2 publications

(1 citation statement)

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“…(Miller et al 2005) applied the C4 algorithm to the second data release of SDSS and presented the "C4 Cluster Catalog" which contains about 2500 clusters and a new sample of 748 clusters of galaxies is identified. Recently, two machine learning algorithms were used to identify galaxy groups and clusters based on galaxy color similarities (Mahmoud et al 2016(Mahmoud et al , 2018.…”

Section: Introductionmentioning

confidence: 99%

Colour similarity study among small galaxy groups members

Fouad

Awad

Shaker

et al. 2019

NRIAG Journal of Astronomy and Geophysics

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A B S T R A C TWe applied a membership test based on the color similarity of group members to detect the discordant galaxies in small groups (quintets) that had been determined by the Friends-of-Friends (FoF) algorithm. Our method depends on the similarity of the color indices (u-g) and (g-r) of the group members. The chosen sample of quintets was extracted from "Fluxand volume-limited groups for SDSS galaxies" catalog which is a spectroscopic sample of galaxies originally taken from the Sloan Digital Sky Survey -Data Release 10 (SDSS-DR10). The sample included 282 quintets with a total number of 1410 galaxies. The similarity measure used in this study is the Euclidean distance. The calculations showed that 73.4% of the group samples (207 out of 282 quintet groups) have galaxies with similar colors (u-g) and (g-r). Each of the remainder groups (75 systems) has an interloper galaxy with different colors than the other members, and hence they became quadrants. We found that group members tend to be more luminous than outliers. We conclude that using the similarities in the color indices between group members gives better identification of group membership.

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Section: Introductionmentioning

confidence: 99%