The Link Between Morphology and Structure of Brightest Cluster Galaxies: Automatic Identification of cDs

TL;DR

This study analyzes a large sample of low-redshift brightest cluster galaxies to link their morphologies with structural properties, enabling automatic classification of cD galaxies with high accuracy using model fitting and parameter space analysis.

Contribution

It introduces an automatic, objective method to distinguish cD from non-cD BCGs based on structural parameters derived from light profile fitting.

Findings

cD galaxies constitute about 57% of the sample.

cDs have larger effective radii and residual flux fractions than ellipticals.

A boundary in the $R_e$--$RFF$ plane effectively separates cD and non-cD BCGs.

Abstract

We study a large sample of 625 low-redshift brightest cluster galaxies (BCGs) and link their morphologies to their structural properties. We derive visual morphologies and find that ~57% of the BCGs are cD galaxies, ~13% are ellipticals, and ~21% belong to the intermediate classes mostly between E and cD. There is a continuous distribution in the properties of the BCG's envelopes, ranging from undetected (E class) to clearly detected (cD class), with intermediate classes (E/cD and cD/E) showing the increasing degrees of the envelope presence. A minority (~7%) of BCGs have disk morphologies, with spirals and S0s in similar proportions, and the rest (~2%) are mergers. After carefully fitting the galaxies light distributions by using one-component (Sersic) and two-component (Sersic+Exponential) models, we find a clear link between the BCGs morphologies and their structures and conclude that a combination of the best-fit parameters derived from the fits can be used to separate cD galaxies from non-cD BCGs. In particular, cDs and non-cDs show very different distributions in the $R_e$--$RFF$ plane, where $R_e$ is the effective radius and $RFF$ (the residual flux fraction) measures the proportion of the galaxy flux present in the residual images after subtracting the models. In general, cDs have larger $R_e$ and $RFF$ values than ellipticals. Therefore we find, in a statistically robust way, a boundary separating cD and non-cD BCGs in this parameter space. BCGs with cD morphology can be selected with reasonably high completeness (~75%) and low contamination (~20%). This automatic and objective technique can be applied to any current or future BCG sample with good quality images.