The Next Generation Virgo Cluster Survey (NGVS). XL. The Morphological Classification of Virgo Cluster Galaxies

TL;DR

This paper introduces a detailed visual classification scheme for Virgo Cluster galaxies based on deep multi-band imaging, capturing a wide range of morphological features and star formation activities across a broad mass spectrum.

Contribution

It presents a new two-component visual classification method that describes galaxy structure and star formation, applicable over six orders of magnitude in stellar mass.

Findings

Good agreement with previous classifications for bright galaxies

Provides catalogs for special galaxy types like ultra-diffuse and compact galaxies

Quantitative classification metrics are measured for a subset of galaxies

Abstract

We present a study of morphologies, based on deep $u^{*}g^{\prime}i^{\prime}z^{\prime}$ imaging of the Virgo Cluster from the Next Generation Virgo Cluster Survey (NGVS), for 3689 Virgo cluster members spanning a mass range of $\sim$$10^{11}M_{\odot}$ to $\sim$$10^5~M_{\odot}$. Our analysis introduces a new, two-component visual classification scheme developed to capture the morphological diversity of galaxies over more than six orders of magnitude in stellar mass. Our morphological classifications use two parameters to describe the global structure and star formation activity of each galaxy. Structural sub-codes denote features such as spiral arms, bars, disks, shells, streams, while star formation sub-codes indicate the form and location of the current star formation activity (e.g., in cores, clumps, filaments, etc). These visual classifications rely on deep $g^\prime$-band images, supplemented by $u^{*}g^{\prime}i^{\prime}$ color images, as well as unsharp-masked images for a subset of objects. We compare our classifications to previous results for bright member galaxies that used more established schemes, finding good agreement. We also measure quantitative classification statistics (e.g., CASGM$_{20}$) for a subset of the brighter galaxies, and present catalogs for some galaxy types of special interest, including structurally compact galaxies, ultra-diffuse galaxies, candidate ultra-compact dwarf transition objects, as well as candidate post-merger systems. These morphological classifications may be useful as a training set in the application of machine learning tools to the next generation of wide-field imaging surveys.