The link between morphology and structure of brightest cluster galaxies: automatic identification of cDsTools Zhao, Dongyao, Aragón-Salamanca, Alfonso and Conselice, Christopher J. (2015) The link between morphology and structure of brightest cluster galaxies: automatic identification of cDs. Monthly Notices of the Royal Astronomical Society, 448 (3). pp. 2530-2545. ISSN 0035-8711 Full text not available from this repository.AbstractWe 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 per cent of the BCGs are cD galaxies, ∼13 per cent are ellipticals, and ∼21 per cent 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 per cent) of BCGs have disc morphologies, with spirals and S0s in similar proportions, and the rest (∼2 per cent) are mergers. After carefully fitting the galaxies light distributions by using one-component (Sérsic) and two-component (Sérsic+Exponential) models, we find a clear link between the BCGs morphologies and their structures and conclude that a combination of the best-fitting 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 Re–RFF plane, where Re 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 Re 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 per cent) and low contamination (∼20 per cent). This automatic and objective technique can be applied to any current or future BCG sample with good-quality images.
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