- ID:
- ivo://CDS.VizieR/J/ApJS/210/3
- Title:
- SDSS bulge, disk and total stellar mass estimates
- Short Name:
- J/ApJS/210/3
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present a catalog of bulge, disk, and total stellar mass estimates for ~660000 galaxies in the Legacy area of the Sloan Digital Sky Survey Data (SDSS) Release 7. These masses are based on a homogeneous catalog of g- and r-band photometry described by Simard et al. (2011, Cat. J/ApJS/196/11), which we extend here with bulge+disk and Sersic profile photometric decompositions in the SDSS u, i, and z bands. We discuss the methodology used to derive stellar masses from these data via fitting to broadband spectral energy distributions (SEDs), and show that the typical statistical uncertainty on total, bulge, and disk stellar mass is ~0.15 dex. Despite relatively small formal uncertainties, we argue that SED modeling assumptions, including the choice of synthesis model, extinction law, initial mass function, and details of stellar evolution likely contribute an additional 60% systematic uncertainty in any mass estimate based on broadband SED fitting. We discuss several approaches for identifying genuine bulge+disk systems based on both their statistical likelihood and an analysis of their one-dimensional surface-brightness profiles, and include these metrics in the catalogs. Estimates of the total, bulge and disk stellar masses for both normal and dust-free models and their uncertainties are made publicly available here.
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- ID:
- ivo://CDS.VizieR/J/AJ/136/2115
- Title:
- SDSS/CIG galaxies classification
- Short Name:
- J/AJ/136/2115
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present a re-evaluation of the optical morphology for 549 galaxies from the Catalog of Isolated Galaxies in the Northern Hemisphere (CIG) that are available in the Sloan Digital Sky Survey (SDSS; DR6). Both the high resolution and high dynamic range of the SDSS images and our semiautomatic image processing scheme allow for a major quality and uniform morphological analysis. The processing scheme includes (1) sky-subtracted, cleaned, and logarithmic scaled g-band images, (2) filtered-enhanced versions of the images in (1), and (3) the corresponding red-green-blue (RGB) composed images available in the SDSS database. We propose an empirical method to distinguishing between E, S0, and Sa candidates through an additional analysis of (4) the surface brightness, position angle, ellipticity and A_4_B_4_ coefficients of the Fourier series expansion profiles. An atlas of mosaics containing (1), (2), and (3) images for Sab-Sm/Irr types and (1), (2), (3), (4) images for E/S0/Sa types was produced and is available on the Web site, http://132.248.1.210.
- ID:
- ivo://CDS.VizieR/J/ApJS/223/20
- Title:
- SDSS-DR8 galaxies classified by WND-CHARM
- Short Name:
- J/ApJS/223/20
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We have applied computer analysis to classify the broad morphological types of ~3x10^6^ Sloan Digital Sky Survey (SDSS) galaxies. For each galaxy, the catalog provides the DR8 object ID, the R.A., the decl., and the certainty for the automatic classification as either spiral or elliptical. The certainty of the classification allows us to control the accuracy of a subset of galaxies by sacrificing some of the least certain classifications. The accuracy of the catalog was tested using galaxies that were classified by the manually annotated Galaxy Zoo catalog. The results show that the catalog contains ~900000 spiral galaxies and ~600000 elliptical galaxies with classification certainty that has a statistical agreement rate of ~98% with the Galaxy Zoo debiased "superclean" data set. The catalog also shows that objects assigned by the SDSS pipeline with a relatively high redshift (z>0.4) can have clear visual spiral morphology.
- ID:
- ivo://CDS.VizieR/J/AJ/142/122
- Title:
- SDSS DR7 galaxy/QSOs pairs
- Short Name:
- J/AJ/142/122
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- The aim of this project is to identify low-redshift host galaxies of quasar absorption-line systems by selecting galaxies that are seen in projection onto quasar sightlines. To this end, we use the Seventh Data Release of the Sloan Digital Sky Survey to construct a parent sample of 97489 galaxy/quasar projections at impact parameters of up to 100 kpc to the foreground galaxy. We then search the quasar spectra for absorption-line systems of CaII and NaI within +/-500km/s1 of the galaxy's velocity. This yields 92 CaII and 16 NaI absorption systems.
- ID:
- ivo://CDS.VizieR/J/A+A/540/A106
- Title:
- SDSS-DR8 groups and clusters of galaxies
- Short Name:
- J/A+A/540/A106
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We create a new catalogue of groups and clusters for the SDSS Data Release 8 sample. We add environmental parameters to our catalogue, together with other galaxy parameters (e.g., morphology), missing from our previous catalogues. We use a modified friends-of-friends (FoF) method with a variable linking length in the transverse and radial directions to eliminate selection effects and to find reliably as many groups as possible to track the supercluster network. We use the groups of galaxies as a basis to determine the luminosity density field. We take into account various selection effects caused by a magnitude limited sample. Our final sample contains 576493 galaxies and 77858 groups. The group catalogue is available at http://www.aai.ee/~elmo/dr8groups/ and from the Strasbourg Astronomical Data Center (CDS).
- ID:
- ivo://CDS.VizieR/J/A+A/514/A102
- Title:
- SDSS DR7 groups of galaxies
- Short Name:
- J/A+A/514/A102
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We extract groups of galaxies as flux-limited and volume-limited samples from the SDSS Data Release 7 to study the supercluster-void network and environmental properties of groups therein. Volume-limited catalogues are particularly useful for comparison of numerical simulations of dark matter halos and the large-scale structure with observations. Extraction of a volume-limited sample of galaxies and groups requires special care to avoid excluding too much observational data. We use a modified friends-of-friends (FoF) method with a slightly variable linking length to obtain a preliminary flux-limited sample. We use the flux-limited groups as the basic sample to include as many galaxies as possible in the volume-limited samples. To determine the scaling of the linking length we calibrated group sizes and mean galaxy number densities within groups by magnitude dilution of a nearby group sub-sample to follow the properties of groups with higher luminosity limits. Our final flux-limited sample contains 78800 groups, and volume-limited subsamples with absolute magnitude limits M_r_=-18, -19, -20, and -21 contain 5463, 12590, 18973, and 9139 groups, respectively, in the DR7 main galaxy main area survey. The spatial number densities of our groups within the subsamples, as well as the mean sizes and rms velocities of our groups practically do not change from sub-sample to sub-sample. This means that the catalogues are homogeneous and well suited for comparison with simulations.
- ID:
- ivo://CDS.VizieR/J/AJ/129/2062
- Title:
- SDSS DR1 isolated galaxies
- Short Name:
- J/AJ/129/2062
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present a new catalog of isolated galaxies obtained through an automated systematic search. These 2980 isolated galaxies were found in ~2099{deg}^2^ of sky in the Sloan Digital Sky Survey Data Release 1 (SDSS DR1, http://www.sdss.org/dr1/) photometry. The selection algorithm, implementing a variation on the criteria developed by Karachentseva in 1973, proved to be very efficient and fast. This catalog will be useful for studies of the general galaxy characteristics. Here we report on our results.
- ID:
- ivo://CDS.VizieR/J/AJ/135/10
- Title:
- SDSS-DR4/RASS source matching
- Short Name:
- J/AJ/135/10
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- The current view of galaxy formation holds that all massive galaxies harbor a massive black hole at their center, but that these black holes are not always in an actively accreting phase. X-ray emission is often used to identify accreting sources, but for galaxies that are not harboring quasars (low-luminosity active galaxies), the X-ray flux may be weak, or obscured by dust. To aid in the understanding of weakly accreting black holes in the local universe, a large sample of galaxies with X-ray detections is needed. We cross-match the ROSAT All Sky Survey (RASS) with galaxies from the Sloan Digital Sky Survey Data Release 4 (SDSS DR4) to create such a sample. Because of the high SDSS source density and large RASS positional errors, the cross-matched catalog is highly contaminated by random associations. We investigate the overlap of these surveys and provide a statistical test of the validity of RASS-SDSS galaxy cross-matches.
- ID:
- ivo://CDS.VizieR/J/AJ/125/1817
- Title:
- SDSS Early-Type Galaxies Catalog
- Short Name:
- J/AJ/125/1817
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- A sample of nearly 9000 early-type galaxies, in the redshift range 0.01<=z<=0.3, was selected from the Sloan Digital Sky Survey (SDSS) using morphological and spectral criteria. The paper describes how the sample was selected, presents examples of images and seeing-corrected fits to the observed surface brightness profiles, describes our method for estimating K-corrections, and shows that the SDSS spectra are of sufficiently high quality to measure velocity dispersions accurately. It also provides catalogs of the measured photometric and spectroscopic parameters. In related papers, these data are used to study how early-type galaxy observables, including luminosity, effective radius, surface brightness, color, and velocity dispersion, are correlated with one another.
- ID:
- ivo://CDS.VizieR/J/A+A/648/A122
- Title:
- SDSS galaxies morphological classification
- Short Name:
- J/A+A/648/A122
- Date:
- 22 Feb 2022
- Publisher:
- CDS
- Description:
- Machine learning methods are effective tools in astronomical tasks for classifying objects by their individual features. One of the promising utilities is related to the morphological classification of galaxies at different redshifts. We use the photometry-based approach for the SDSS data (1) to exploit five supervised machine learning techniques and define the most effective among them for the automated galaxy morphological classification; (2) to test the influence of photometry data on morphology classification; (3) to discuss problem points of supervised machine learning and labeling bias; and (4) to apply the best fitting machine learning methods for revealing the unknown morphological types of galaxies from the SDSS DR9 at z<0.1. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, naive Bayes, logistic regression, support-vector machine, random forest, k-nearest neighbors. We present the results of a binary automated morphological classification of galaxies conducted by human labeling, multi-photometry, and five supervised machine learning methods. We applied it to the sample of galaxies from the SDSS DR9 with redshifts of 0.02<z<0.1 and absolute stellar magnitudes of -24mag<Mr<-19.4mag. For the analysis we used absolute magnitudes Mu, Mg, Mr, Mi, Mz; color indices Mu-Mr, Mg-Mi, Mu-Mg, Mr-Mz; and the inverse concentration index to the center R50/R90. We determined the ability of each method to predict the morphological type, and verified various dependencies of the method's accuracy on redshifts, human labeling, morphological shape, and overlap of different morphological types for galaxies with the same color indices. We find that the morphology based on the supervised machine learning methods trained over photometric parameters demonstrates significantly less bias than the morphology based on citizen-science classifiers. The support-vector machine and random forest methods with Scikit-learn software machine learning library in Python provide the highest accuracy for the binary galaxy morphological classification. Specifically, the success rate is 96.4% for support-vector machine (96.1% early E and 96.9% late L types) and 95.5% for random forest (96.7% early E and 92.8% late L types). Applying the support-vector machine for the sample of 316 031 galaxies from the SDSS DR9 at z<0.1 with unknown morphological types, we found 139659 E and 176372 L types among them.
