Measuring Photometric Properties Of Sdss And Manga Galaxies

A number of recent estimates of the total luminosities of galaxies in the SDSS are significantly larger than those reported by the Sloan Digital Sky Survey (SDSS) pipeline. This is because of a combination of three effects: one is simply a matter of defining the scale out to which one integrates the fit when defining the total luminosity, and amounts on average to \u3c= 0.1 mag even for the most luminous galaxies. The other two are less trivial and tend to be larger; they are due to differences in how the background sky is estimated and what model is fit to the surface brightness profile. Using the SDSS sky biases luminosities by more than a few tenths of a magnitude for objects with half-light radii \u3e= 7 arcsec. In the SDSS main galaxy sample, these are typically luminous galaxies, so they are not necessarily nearby. It is shown that PyMorph fits of Meert et al. 2015 to DR7 data remain valid for DR9 images. These findings show that, especially at large luminosities, PyMorph estimates should be preferred to the SDSS pipeline values as they provide a better fit to the surface brightness profiles of galaxies. It is natural to wonder if the difference between PyMorph and SDSS is due to intracluster light (ICL). The effect of PyMorph reductions vs SDSS can be compared to the full sample as in small group environments, and for satellites in the most massive clusters as well to show that the effect is the same. None of these are expected to be significantly affected by ICL. The only additional effect for centrals is found in the most massive haloes, but it is argued that even this is not dominated by ICL. Hence, for the vast majority of galaxies, the differences between PyMorph and SDSS pipeline photometry cannot be ascribed to the semantics of whether or not one includes the ICL when describing the stellar mass of massive galaxies. After checking that PyMorph sky estimates should still be used over SDSS pipeline values, it was found prudent to create the catalog MaNGA PyMorph Photometric Value Added Catalog (MPP-VAC) for the SDSS-IV MaNGA survey galaxies. The MPP-VAC provides photometric parameters obtained from Sersic and Sersic+Exponential fits from PyMorph for the MaNGA DR15 galaxy sample. Moving forward from the initial PyMorph comparisons of this work, the MPP-VAC sample incorporates three improvements: it uses the most recent reduction of SDSS images; it uses slightly modified criteria for determining bulge-to-disk decompositions; and finally, all the fits in MPP-VAC have been eye-balled, and re-fit if necessary, for additional reliability. Its companion catalog, the MDLM-VAC, provides Deep Learning-based morphological classifications for the same galaxies. The morphological classification includes a series of Galaxy Zoo-like questions plus a TType and a finer separation between elliptical (E) and S0 galaxies. Some work in the literature suggests that there is little correlation between the Sersic index of the bulge component and the morphology of these galaxies. The information from the MPP- and MDLM-VACs were combined with MaNGA\u27s spatially resolved spectroscopy to study how the stellar angular momentum depends on morphological type. Strong correlations between stellar kinematics, photometric properties, and morphological type were found. Lastly, this dissertation shows how proper luminosity measurements are imperative for determining the stellar mass function of a galaxy. Additionally, the proper inclusion of M/L gradients within galaxies is imperative to mass estimates. This is of great importance to galaxy formation and evolution models

The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data

The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data

The fifteenth data release of the Sloan Digital Sky Surveys : first release of MaNGA derived quantities, data visualization tools and stellar library

Twenty years have passed since first light for the Sloan Digital SkySurvey (SDSS). Here, we release data taken by the fourth phase of SDSS(SDSS-IV) across its first three years of operation (July 2014-July2017). This is the third data release for SDSS-IV, and the fifteenth from SDSS (Data Release Fifteen; DR15). New data come from MaNGA - we release 4824 datacubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g. stellar and gas kinematics, emission line, andother maps) from the MaNGA Data Analysis Pipeline (DAP), and a new data visualisation and access tool we call "Marvin". The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials and examples of data use. While SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V(2020-2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data.Publisher PDFPeer reviewe