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Register a new userCatalogue with visual morphological classification of 32,616 radio galaxies with optical hosts
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We present the catalogue of Radio sources associated with Optical Galaxies and having Unresolved or Extended morphologies I (ROGUE I). It was generated by cross-matching galaxies from the Sloan Digital Sky Survey Data Release 7 (SDSS DR 7) as well as radio sources from the First Images of Radio Sky at Twenty Centimetre (FIRST) and the National Radio Astronomical Observatory VLA Sky Survey (NVSS) catalogues. We created the largest handmade catalogue of visually classified radio objects and associated with them optical host galaxies, containing 32,616 galaxies with a FIRST core within 3 arcsec of the optical position. All listed objects possess the good quality SDSS DR 7 spectra with the signal-to-noise ratio $>$10 and spectroscopic redshifts up to $z=0.6$. The radio morphology classification was performed by a visual examination of the FIRST and the NVSS contour maps overlaid on a DSS image, while an optical morphology classification was based on the 120 arcsec snapshot images from SDSS DR 7. The majority of radio galaxies in ROGUE I, i.e. $sim$ 93%, are unresolved (compact or elongated), while the rest of them exhibit extended morphologies, such as Fanaroff-Riley (FR) type I, II, and hybrid, wide-angle tail, narrow-angle tail, head-tail sources, and sources with intermittent or reoriented jet activity, i.e. double-double, X-shaped, and Z-shaped. Most of FR IIs have low radio luminosities, comparable to the luminosities of FR Is. Moreover, due to visual check of all radio maps and optical images, we were able to discover or reclassify a number of radio objects as giant, double-double, X-shaped, and Z-shaped radio galaxies. The presented sample can serve as a database for training automatic methods of identification and classification of optical and radio galaxies.
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We present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million of galaxies, using the Dark Energy Survey (DES) Year 3 data based on Convolutional Neural Networks (CNN). Monochromatic $i$-band DES images with linear, logarithmic, and gradient scales, matched with debiased visual classifications from the Galaxy Zoo 1 (GZ1) catalogue, are used to train our CNN models. With a training set including bright galaxies ($16le{i}<18$) at low redshift ($z<0.25$), we furthermore investigate the limit of the accuracy of our predictions applied to galaxies at fainter magnitude and at higher redshifts. Our final catalogue covers magnitudes $16le{i}<21$, and redshifts $z<1.0$, and provides predicted probabilities to two galaxy types -- Ellipticals and Spirals (disk galaxies). Our CNN classifications reveal an accuracy of over 99% for bright galaxies when comparing with the GZ1 classifications ($i<18$). For fainter galaxies, the visual classification carried out by three of the co-authors shows that the CNN classifier correctly categorises disky galaxies with rounder and blurred features, which humans often incorrectly visually classify as Ellipticals. As a part of the validation, we carry out one of the largest examination of non-parametric methods, including $sim$100,000 galaxies with the same coverage of magnitude and redshift as the training set from our catalogue. We find that the Gini coefficient is the best single parameter discriminator between Ellipticals and Spirals for this data set.
We present a host morphological study of 1,265 far-infrared galaxies (FIRGs) and sub-millimeter galaxies (SMGs) in the Cosmic Evolution Survey field using the F160W and F814W images obtained by the Hubble Space Telescope. The FIRGs and the SMGs are selected from the Herschel Multi-tiered Extragalactic Survey and the SCUBA-2 Cosmology Legacy Survey, respectively. Their precise locations are based on the interferometry data from the Atacama Large Millimeter/submillimeter Array and the Very Large Array. The vast majority of these objects are at $0.1lesssim zlesssim 3$. While the SMGs do not constitute a subset of the FIRGs in our selection due to the signal-to-noise ratio thresholds, SMGs can be regarded as the population at the high-redshift tail of FIRGs. Most of our FIRGs/SMGs have total infrared luminosity ($L_{rm IR}$) in the regimes of luminous and ultra-luminous infrared galaxies (LIRGs, $L_{rm IR} = 10^{11-12}L_odot$; ULIRGs, $L_{rm IR}>10^{12}L_odot$). The hosts of the SMG ULIRGs, FIRG ULIRGs and FIRG LIRGs are of sufficient numbers to allow for detailed analysis, and they are only modestly different in their stellar masses. Their morphological types are predominantly disk galaxies (type D) and irregular/interacting systems (type Irr/Int). There is a morphological transition at $zapprox 1.25$ for the FIRG ULIRG hosts, above which the Irr/Int galaxies dominate and below which the D and the Irr/Int galaxies have nearly the same contributions. The SMG ULIRG hosts seem to experience a similar transition. This suggests a shift in the relative importance of galaxy mergers/interactions versus secular gas accretions in normal disk galaxies as the possible triggering mechanisms of ULIRGs. The FIRG LIRG hosts are predominantly D galaxies over $z=0.25-1.25$ where they are of sufficient statistics.
Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological classification. We train and test several convolutional neural network architectures to classify the morphologies of galaxies in both a 3-class (elliptical, lenticular, spiral) and 4-class (+irregular/miscellaneous) schema with a dataset of 14034 visually-classified SDSS images. We develop a new CNN architecture that outperforms existing models in both 3 and 4-way classification, with overall classification accuracies of 83% and 81% respectively. We also compare the accuracies of 2-way / binary classifications between all four classes, showing that ellipticals and spirals are most easily distinguished (>98% accuracy), while spirals and irregulars are hardest to differentiate (78% accuracy). Through an analysis of all classified samples, we find tentative evidence that misclassifications are physically meaningful, with lenticulars misclassified as ellipticals tending to be more massive, among other trends. We further combine our binary CNN classifiers to perform a hierarchical classification of samples, obtaining comparable accuracies (81%) to the direct 3-class CNN, but considerably worse accuracies in the 4-way case (65%). As an additional verification, we apply our networks to a small sample of Galaxy Zoo images, obtaining accuracies of 92%, 82% and 77% for the binary, 3-way and 4-way classifications respectively.
We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modifie
We present optical (~3200A to ~9000A) off-nuclear spectra of 26 powerful active galaxies in the redshift range 0.1 < z < 0.3, obtained with the Mayall and William Herschel 4-meter class telescopes. The sample consists of radio-quiet quasars, radio-loud quasars (all with -23 > M_V > -26) and radio galaxies of Fanaroff & Riley Type II (with extended radio luminosities and spectral indices comparable to those of the radio-loud quasars). The spectra were all taken approximately 5 arcseconds off-nucleus, with offsets carefully selected so as to maximise the amount of galaxy light falling into the slit, whilst simultaneously minimising the amount of scattered nuclear light. The majority of the resulting spectra appear to be dominated by the integrated stellar continuum of the underlying galaxies rather than by light from the non-stellar processes occurring in the active nuclei, and in many cases a 4000A break feature can be identified. The individual spectra are described in detail, and the importance of the various spectral components is discussed. Stellar population synthesis modelling of the spectra will follow in a subsequent paper (Nolan et al. 2000).
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