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This paper is the third which examines galaxy morphology from the point of view of comprehensive de Vaucouleurs revised Hubble-Sandage (CVRHS) classification, a variation on the original de Vaucouleurs classification volume that accounts for finer details of galactic structure, including lenses, nuclear structures, embedded disks, boxy and disky components, and other features. The classification is applied to the EFIGI sample, a well-defined set of nearby galaxies which were previously examined by Baillard et al. and de Lapparent et al. The survey is focussed on statistics of features, and brings attention to exceptional examples of some morphologies, such as skewed bars, blue bar ansae, bar-outer pseudoring misalignment, extremely elongated inner SB rings, outer rings and lenses, and other features that are likely relevant to galactic secular evolution and internal dynamics. The possibility of using these classifications as a training set for automated classification algorithms is also discussed.
Spitzer Space Telescope Infrared Array Camera (IRAC) imaging provides an opportunity to study all known morphological types of galaxies in the mid-IR at a depth significantly better than ground-based near-infrared and optical images. The goal of this
We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (
IRAS flux densities, redshifts, and infrared luminosities are reported for all sources identified in the IRAS Revised Bright Galaxy Sample (RBGS), a complete flux-limited survey of all extragalactic objects with total 60 micron flux density greater t
The IRAS Revised Bright Galaxy Sample (RBGS) comprises galaxies and unresolved mergers stronger than $S = 5.24$ Jy at $lambda = 60~mumathrm{m}$ with galactic latitudes $vert b vert > 5^circ$. Nearly all are dusty star-forming galaxies whose radio con
We present a method to model optical images of galaxies using Expectation Maximization Principal Components Analysis (EMPCA). The method relies on the data alone and does not assume any pre-established model or fitting formula. It preserves the stati