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Register a new userThe Systematics of Galaxy Morphology in the Comprehensive de Vaucouleurs revised Hubble-Sandage Classification System: Application to the EFIGI Sample
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Ronald J. Buta
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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.
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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 study is to examine the imprint of the de Vaucouleurs classification volume in the 3.6 micron band, which is the best Spitzer waveband for galactic stellar mass morphology owing to its depth and its reddening-free sensitivity mainly to older stars. For this purpose, we have prepared classification images for 207 galaxies from the Spitzer archive, most of which are formally part of the Spitzer Survey of Stellar Structure in Galaxies (S^4G), a Spitzer post-cryogenic (warm) mission Exploration Science Legacy Program survey of 2,331 galaxies closer than 40 Mpc. For the purposes of morphology, the galaxies are interpreted as if the images are {it blue light}, the historical waveband for classical galaxy classification studies. We find that 3.6 micron classifications are well-correlated with blue-light classifications, to the point where the essential features of many galaxies look very similar in the two very different wavelength regimes. Drastic differences are found only for the most dusty galaxies. Consistent with a previous study by Eskridge et al. (2002), the main difference between blue light and mid-IR types is an approximately 1 stage interval difference for S0/a to Sbc or Sc galaxies, which tend to appear earlier in type at 3.6 microns due to the slightly increased prominence of the bulge, the reduced effects of extinction, and the reduced (but not completely eliminated) effect of the extreme population I stellar component. We present an atlas of all of the 207 galaxies analyzed here, and bring attention to special features or galaxy types that are particularly distinctive in the mid-IR.
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: (1) consideration of the clustering performance simultaneously when learning features from images; (2) allowing for various distance thresholds within the HC algorithm; (3) using the galaxy orientation to determine the number of clusters. This setup provides 27 clusters created with this unsupervised learning which we show are well separated based on galaxy shape and structure (e.g., Sersic index, concentration, asymmetry, Gini coefficient). These resulting clusters also correlate well with physical properties such as the colour-magnitude diagram, and span the range of scaling-relations such as mass vs. size amongst the different machine-defined clusters. When we merge these multiple clusters into two large preliminary clusters to provide a binary classification, an accuracy of $sim87%$ is reached using an imbalanced dataset, matching real galaxy distributions, which includes 22.7% early-type galaxies and 77.3% late-type galaxies. Comparing the given clusters with classic Hubble types (ellipticals, lenticulars, early spirals, late spirals, and irregulars), we show that there is an intrinsic vagueness in visual classification systems, in particular galaxies with transitional features such as lenticulars and early spirals. Based on this, the main result in this work is not how well our unsupervised method matches visual classifications and physical properties, but that the method provides an independent classification that may be more physically meaningful than any visually based ones.
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 than 5.24 Jy, covering the entire sky surveyed by IRAS at Galactic latitude |b| > 5 degrees. The RBGS includes 629 objects, with a median (mean) sample redshift of 0.0082 (0.0126) and a maximum redshift of 0.0876. The RBGS supersedes the previous two-part IRAS Bright Galaxy Samples, which were compiled before the final (Pass 3) calibration of the IRAS Level 1 Archive in May 1990. The RBGS also makes use of more accurate and consistent automated methods to measure the flux of objects with extended emission. Basic properties of the RBGS sources are summarized, including estimated total infrared luminosities, as well as updates to cross-identifications with sources from optical galaxy catalogs established using the NASA/IPAC Extragalactic Database (NED). In addition, an atlas of images from the Digitized Sky Survey with overlays of the IRAS position uncertainty ellipse and annotated scale bars is provided for ease in visualizing the optical morphology in context with the angular and metric size of each object. The revised bolometric infrared luminosity function, phi(L_ir), for infrared bright galaxies in the local Universe remains best fit by a double power law, phi(L_ir) ~ L_ir^alpha, with alpha = -0.6 (+/- 0.1), and alpha = -2.2 (+/- 0.1) below and above the characteristic infrared luminosity L_ir ~ 10^{10.5} L_solar, respectively. (Abridged)
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 continuum and far-infrared luminosities are proportional to their current rates of star formation. We used the MeerKAT array of 64 dishes to make $5 times 3$ min snapshot observations at $ u = 1.28$ GHz covering all 298 southern (J2000 $delta < 0^circ$) RBGS sources identified with external galaxies. The resulting images have $theta approx 7.5$ arcsec FHWM resolution and rms fluctuations $sigma approx 20~mumathrm{Jy~beam}^{-1} approx 0.26$ K, low enough to reveal even faint disk emission. The rms position uncertainties are $sigma_alpha approx sigma_delta approx 1$ arcsec relative to accurate near-infrared positions, and the image dynamic ranges are DR $gtrsim 10^4:1$.
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 statistical properties of the sample, minimizing possible biases. The precision of the reconstructions appears to be suited for photometric, morphological and weak lensing analysis, as well as the realization of mock astronomical images. Here, we put some emphasis on the latter because weak gravitational lensing is entering a new phase in which systematics are becoming the major source of uncertainty. Accurate simulations are necessary to perform a reliable calibration of the ellipticity measurements on which the final bias depends. As a test case, we process $7038$ galaxies observed with the ACS/WFC stacked images of the Hubble eXtreme Deep Field (XDF) and measure the accuracy of the reconstructions in terms of their moments of brightness, which turn out to be comparable to what can be achieved with well-established weak-lensing algorithms.
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