- ID:
- ivo://CDS.VizieR/J/MNRAS/406/1595
- Title:
- Scalelength of 30000 SDSS disc galaxies
- Short Name:
- J/MNRAS/406/1595
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Disc scalelength (h) for 30000 galaxies from the Sloan Digitized Sky Survey (SDSS) Data Release 7, in the r-band. Also included is the Asymmetry parameter for each galaxy. Virtual Observatory methods and tools were used to define, retrieve and analyze the images for this unprecedentedly large sample classified as spiral galaxies in the LEDA catalogue. These parameters are also available for all other SDSS bands (u,g,i,z), and they can be retrieved from the Author. An extensive discussion about the errors involved in the derived parameters can be found in Fathi et al. (2010MNRAS.406.1595F) and Fathi (2010ApJ...722L.120F)
Number of results to display per page
Search Results
- ID:
- ivo://CDS.VizieR/J/A+A/525/A157
- Title:
- SDSS automated morphology classification
- Short Name:
- J/A+A/525/A157
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present an automated morphological classification in 4 types (E, S0, Sab, Scd) of ~700 000 galaxies from the SDSS DR7 spectroscopic sample based on support vector machines. The main new property of the classification is that we associate a probability to each galaxy of being in the four morphological classes instead of assigning a single class. The classification is therefore better adapted to nature where we expect a continuous transition between different morphological types. The algorithm is trained with a visual classification and then compared to several independent visual classifications including the Galaxy Zoo first-release catalog. We find a very good correlation between the automated classification and classical visual ones.
- 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.
- 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/MNRAS/422/25
- Title:
- SDSS DR7 groups, clusters and filaments
- Short Name:
- J/MNRAS/422/25
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We have developed a multiscale structure identification algorithm for the detection of overdensities in galaxy data that identifies structures having radii within a user-defined range. Our "multiscale probability mapping" technique combines density estimation with a shape statistic to identify local peaks in the density field. This technique takes advantage of a user-defined range of scale sizes, which are used in constructing a coarse-grained map of the underlying fine-grained galaxy distribution, from which overdense structures are then identified. In this study we have compiled a catalogue of groups and clusters at 0.025<z<0.24 based on the Sloan Digital Sky Survey (SDSS), Data Release 7, quantifying their significance and comparing with other catalogues. Most measured velocity dispersions for these structures lie between 50 and 400km/s. A clear trend of increasing velocity dispersion with radius from 0.2 to 1Mpc/h is detected, confirming the lack of a sharp division between groups and clusters. A method for quantifying elongation is also developed to measure the elongation of group and cluster environments. By using our group and cluster catalogue as a coarse-grained representation of the galaxy distribution for structure sizes of <~1Mpc/h, we identify 53 filaments (from an algorithmically derived set of 100 candidates) as elongated unions of groups and clusters at 0.025<z<0.13. These filaments have morphologies that are consistent with previous samples studied.
- ID:
- ivo://CDS.VizieR/J/ApJS/220/3
- Title:
- SDSS-DR7 isolated galaxy morphologies
- Short Name:
- J/ApJS/220/3
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Isolated galaxies in low-density regions are significant in the sense that they are least affected by the hierarchical pattern of galaxy growth and interactions with perturbers, at least for the last few gigayears. To form a comprehensive picture of the star-formation history of isolated galaxies, we constructed a catalog of isolated galaxies and their comparison sample in relatively denser environments. The galaxies are drawn from the Sloan Digital Sky Survey Data Release 7 in the redshift range of 0.025<z<0.044. We performed a visual inspection and classified their morphology following the Hubble classification scheme. For the spectroscopic study, we make use of the catalog provided by Oh et al. (2011ApJS..195...13O). We confirm most of the earlier understanding on isolated galaxies. The most remarkable additional results are as follows. Isolated galaxies are dominantly late type with the morphology distribution (E:S0:S:Irr)=(9.9:11.3:77.6:1.2)%. The frequency of elliptical galaxies among isolated galaxies is only a third of that of the comparison sample. Most of the photometric and spectroscopic properties are surprisingly similar between the isolated and comparison samples. However, early-type isolated galaxies are less massive by 50% and younger (by H{beta}) by 20% than their counterparts in the comparison sample. This can be explained as a result of different merger and star-formation histories for differing environments in the hierarchical merger paradigm.
- ID:
- ivo://CDS.VizieR/J/MNRAS/477/894
- Title:
- SDSS galaxies classification
- Short Name:
- J/MNRAS/477/894
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network that is easy to use and provides good accuracy. In our study, we use a sample of 9346 galaxies in the redshift range of 0.009-0.2 from the Sloan Digital Sky Survey (SDSS), which has 3864 barred galaxies, the rest being unbarred. We reach a top precision of 94 per cent in identifying bars in galaxies using the trained network. This accuracy matches the accuracy reached by human experts on the same data without additional information about the images. Since deep convolutional neural networks can be scaled to handle large volumes of data, the method is expected to have great relevance in an era where astronomy data is rapidly increasing in terms of volume, variety, volatility, and velocity along with other V's that characterize big data. With the trained model, we have constructed a catalogue of barred galaxies from SDSS and made it available online.
- 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.
- ID:
- ivo://CDS.VizieR/J/MNRAS/446/3749
- Title:
- SDSS nearby galaxies morphologies
- Short Name:
- J/MNRAS/446/3749
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We investigate box/peanut and bar structures in image data of edge-on and face-on nearby galaxies taken from the Sloan Digital Sky Survey (SDSS) to present catalogues containing the surface brightness parameters and the morphology classification. About 1700 edge-on galaxies and 2600 face-on galaxies are selected from SDSS DR7 in the g, r and i-bands. The images of each galaxy are fitted with the model of two-dimensional surface brightness of the Sersic bulge and exponential disk. After removing some irregular data, the box/peanut, bar and other structures are easily distinguished by eye using residual (observed minus model) images. We find 292 box/peanut structures in the 1329 edge-on samples and 630 bar structures in 1890 face-on samples in the i-band, after removing some irregular data. The fraction of box/peanut galaxies is about 22 per cent against the edge-on samples, and that of bar galaxies is about 33 per cent (about 50 per cent if 629 elliptical galaxies are removed) against the face-on samples. Furthermore the strengths of the box/peanuts and bars are evaluated as strong, standard or weak. We find that the strength increases slightly with increasing B/T (bulge-to-total flux ratio), and that the fraction of box/peanuts is generally about a half of that of bars, irrespective of the strength and B/T. Our result supports the idea that a box/peanut is a bar seen edge-on.
