SDSS-IV MaNGA: Complete census of massive slow-rotator early-type galaxy candidates and their environment in a volume-limited sample

TL;DR

This study provides the largest complete census of massive slow-rotator early-type galaxy candidates and their environments, using stellar kinematics and a novel visual classification method to improve understanding of galaxy evolution.

Contribution

It introduces a new visual classification method for galaxy kinematic types and combines spectroscopic and photometric data to create the largest complete census of slow rotators in nearby galaxy groups and clusters.

Findings

False negative rate of ~6% in classification

False positive rate of about 50%, implying many candidates are confirmed as fast rotators with stellar kinematics

Largest sample of massive slow rotator candidates and their environments to date

Abstract

Galaxy morphology is inextricably linked to environment. The morphology-density relation quantifies this relationship. However, optical morphology is only loosely related to the kinematic structure of galaxies, and about two thirds of elliptical ("spheroidal") galaxies are actually misclassified face-on disks, and would appear flattened if view edge on. A more robust classification is the slow/fast rotator classification which describes the kinematic structure. Slow and fast rotators form a bimodality in galaxy properties and are thought to follow distinct evolutionary paths, and so a kinematic morphology-density (kT-$\Sigma$) relation is more meaningful. To date the kT-$\Sigma$ relation has only been studied for a handful of nearby clusters,or across large numbers of clusters but with incomplete coverage. Here, we combine stellar kinematics obtained with the Sloan Digital Sky Survey's (SDSS) Mapping Nearby Galaxies at Apache Point Observatory survey with classifications using a novel visual method to obtain the largest complete census of slow and fast rotators in nearby galaxy groups and clusters. To account for incompleteness in the SDSS spectroscopic catalogue, we combine the catalogue with the photometric catalogue, which we clean using empirical criteria. We test our visual classification method and find our false negative rate to be only $\sim$6% (0% for $M \gtrsim 10^{11.7}M_{\odot}$). In contrast, our false positive rate is about 50% implying half of all slow rotator candidates will be confirmed as fast rotators if stellar kinematics become available. Hence, our misclassification is essentially random with only a weak dependence on stellar mass, and as slow rotators are intrinsically rare, the absolute number of misclassifications will be small compared to the sample size. The result is the largest complete census of massive slow rotator candidates and their environments to date.