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We present the first nonparametric morphological analysis of a set of spiral galaxies from UV to submm wavelengths. Our study is based on high-quality multi-wavelength imaging for nine well-resolved spiral galaxies from the DustPedia database, combined with nonparametric morphology indicators calculated in a consistent way using the {tt{StatMorph}} package. We measure the half-light radius, the concentration index, the asymmetry index, the smoothness index, the Gini coefficient and the $M_{20}$ indicator in various wavebands from UV to submm wavelengths, as well as in stellar mass, dust mass and star formation rate maps. We find that the interstellar dust in galaxies is distributed in a more extended, less centrally concentrated, more asymmetric, and more clumpy way than the stars. This is particularly evident when comparing morphological indicators based on the stellar mass and dust mass maps. This should serve as a warning sign against treating the dust in galaxies as a simple smooth component. We argue that the nonparametric galaxy morphology of galaxies from UV to submm wavelengths is an interesting test for cosmological hydrodynamics simulations.
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(abridged) We present STARDUST, a new self-consistent modelling of the spectral energy distributions (SEDs) of galaxies from far-UV to radio wavelengths. In order to derive the SEDs in this broad spectral range, we first couple spectrophotometric and (closed-box) chemical evolutions to account for metallicity effects on the spectra of synthetic stellar populations. We then use a phenomenological fit for the metal-dependent extinction curve and a simple geometric distribution of the dust to compute the optical depth of galaxies and the corresponding obscuration curve. This enables us to calculate the fraction of stellar light reprocessed in the infrared range. In a final step, we define a dust model with various components and we fix the weights of these components in order to reproduce the IRAS correlation of IR colours with total IR luminosities. This allows us to compute far-IR SEDs that phenomenologically mimic observed trends. We are able to predict the spectral evolution of galaxies in a broad wavelength range, and we can reproduce the observed SEDs of local spirals, starbursts, luminous infrared galaxies (LIRGs) and ultra luminous infrared galaxies (ULIRGs). This modelling is so far kept as simple as possible and depends on a small number of free parameters, namely the initial mass function (IMF), star formation rate (SFR) time scale, gas density, and galaxy age, as well as on more refined assumptions on dust properties and the presence (or absence) of gas inflows/outflows.
Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.
We have undertaken an imaging survey of 34 nearby galaxies in far-ultraviolet (FUV, ~1500A) and optical (UBVRI) passbands to characterize galaxy morphology as a function of wavelength. This sample, which includes a range of classical Hubble types from elliptical to irregular with emphasis on spirals at low inclination angle, provides a valuable database for comparison with images of high-z galaxies whose FUV light is redshifted into the optical and near- infrared bands. Ultraviolet data are from the UIT Astro-2 mission. We present images and surface brightness profiles for each galaxy, and we discuss the wavelength-dependence of morphology for different Hubble types in the context of understanding high-z objects. In general, the dominance of young stars in the FUV produces the patchy appearance of a morphological type later than that inferred from optical images. Prominent rings and circumnuclear star formation regions are clearly evident in FUV images of spirals, while bulges, bars, and old, red stellar disks are faint to invisible at these short wavelengths. However, the magnitude of the change in apparent morphology ranges from dramatic in early--type spirals with prominent optical bulges to slight in late-type spirals and irregulars, in which young stars dominate both the UV and optical emission. Starburst galaxies with centrally concentrated, symmetric bursts display an apparent ``E/S0 structure in the FUV, while starbursts associated with rings or mergers produce a peculiar morphology. We briefly discuss the inadequacy of the optically-defined Hubble sequence to describe FUV galaxy images and estimate morphological k-corrections, and we suggest some directions for future research with this dataset.
We present redshift-zero synthetic observational data considering dust attenuation and dust emission for the thirty galaxies of the Auriga project, calculated with the SKIRT radiative transfer code. The post-processing procedure includes components for star-forming regions, stellar sources, and diffuse dust taking into account stochastic heating of dust grains. This allows us to obtain realistic high-resolution broadband images and fluxes from ultraviolet to sub-millimeter wavelengths. For the diffuse dust component, we consider two mechanisms for assigning dust to gas cells in the simulation. In one case, only the densest or the coldest gas cells are allowed to have dust, while in the other case this condition is relaxed to allow a larger number of dust-containing cells. The latter approach yields galaxies with a larger radial dust extent and an enhanced dust presence in the inter-spiral regions. At a global scale, we compare Auriga galaxies with observations by deriving dust scaling relations using SED fitting. At a resolved scale, we make a multi-wavelength morphological comparison with nine well-resolved spiral galaxies from the DustPedia observational database. We find that for both dust assignment methods, although the Auriga galaxies show a good overall agreement with observational dust properties, they exhibit a slightly higher specific dust mass. The multi-wavelength morphological analysis reveals a good agreement between the Auriga and the observed galaxies in the optical wavelengths. In the mid and far-infrared wavelengths, Auriga galaxies appear smaller and more centrally concentrated in comparison to their observed counterparts. We publicly release the multi-observer images and fluxes in 50 commonly used broadband filters.
We study the spatially-resolved stellar specific angular momentum $j_*$ in a high-quality sample of 24 CALIFA galaxies covering a broad range of visual morphology, accounting for stellar velocity and velocity dispersion. The shape of the spaxel-wise probability density function of normalised $s=j_*/j_{*mean}$, PDF($s$), deviates significantly from the near-universal initial distribution expected of baryons in a dark matter halo and can be explained by the expected baryonic effects in galaxy formation that remove and redistribute angular momentum. Further we find that the observed shape of the PDF($s$) correlates significantly with photometric morphology, where late-type galaxies have PDF($s$) that is similar to a normal distribution, whereas early types have a strongly-skewed PDF($s$) resulting from an excess of low-angular momentum material. Galaxies that are known to host pseudobulges (bulge Sersic index $n_b <2.2$) tend to have less skewed bulge PDF($s$), with skewness $(b_{1rb})lesssim0.8$. The PDF($s$) encodes both kinematic and photometric information and appears to be a robust tracer of morphology. Its use is motivated by the desire to move away from traditional component-based classifications which are subject to observer bias, to classification on a galaxys fundamental (stellar mass, angular momentum) properties. In future, PDF($s$) may also be useful as a kinematic decomposition tool.
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