Source Matching in the SDSS and RASS: Which Galaxies are Really X-ray Sources?

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. SDSS quasars provide a test of our cross-match validation scheme, as they have a very high fraction of true RASS matches. We find that the number of true matches between the SDSS main galaxy sample and the RASS is highly dependent on the optical spectral classification of the galaxy; essentially no star-forming galaxies are detected, while more than 0.6% of narrow-line Seyferts are detected in the RASS. Also, galaxies with ambiguous optical classification have a surprisingly high RASS detection fraction. This allows us to further constrain the SEDs of low-luminosity active galaxies. Our technique is quite general, and can be applied to any cross-matching between surveys with well-understood positional errors.Comment: 10 pages, 10 figures, submitted to The Astronomical Journal on 19 June 200

Investigating interoperability of the LSST Data Management software stack with Astropy

The Large Synoptic Survey Telescope (LSST) will be an 8.4m optical survey telescope sited in Chile and capable of imaging the entire sky twice a week. The data rate of approximately 15TB per night and the requirements to both issue alerts on transient sources within 60 seconds of observing and create annual data releases means that automated data management systems and data processing pipelines are a key deliverable of the LSST construction project. The LSST data management software has been in development since 2004 and is based on a C++ core with a Python control layer. The software consists of nearly a quarter of a million lines of code covering the system from fundamental WCS and table libraries to pipeline environments and distributed process execution. The Astropy project began in 2011 as an attempt to bring together disparate open source Python projects and build a core standard infrastructure that can be used and built upon by the astronomy community. This project has been phenomenally successful in the years since it has begun and has grown to be the de facto standard for Python software in astronomy. Astropy brings with it considerable expectations from the community on how astronomy Python software should be developed and it is clear that by the time LSST is fully operational in the 2020s many of the prospective users of the LSST software stack will expect it to be fully interoperable with Astropy. In this paper we describe the overlap between the LSST science pipeline software and Astropy software and investigate areas where the LSST software provides new functionality. We also discuss the possibilities of re-engineering the LSST science pipeline software to build upon Astropy, including the option of contributing affliated packages

Investigating interoperability of the LSST Data Management software stack with Astropy

The Large Synoptic Survey Telescope (LSST) will be an 8.4m optical survey telescope sited in Chile and capable of imaging the entire sky twice a week. The data rate of approximately 15TB per night and the requirements to both issue alerts on transient sources within 60 seconds of observing and create annual data releases means that automated data management systems and data processing pipelines are a key deliverable of the LSST construction project. The LSST data management software has been in development since 2004 and is based on a C++ core with a Python control layer. The software consists of nearly a quarter of a million lines of code covering the system from fundamental WCS and table libraries to pipeline environments and distributed process execution. The Astropy project began in 2011 as an attempt to bring together disparate open source Python projects and build a core standard infrastructure that can be used and built upon by the astronomy community. This project has been phenomenally successful in the years since it has begun and has grown to be the de facto standard for Python software in astronomy. Astropy brings with it considerable expectations from the community on how astronomy Python software should be developed and it is clear that by the time LSST is fully operational in the 2020s many of the prospective users of the LSST software stack will expect it to be fully interoperable with Astropy. In this paper we describe the overlap between the LSST science pipeline software and Astropy software and investigate areas where the LSST software provides new functionality. We also discuss the possibilities of re-engineering the LSST science pipeline software to build upon Astropy, including the option of contributing affliated packages

SDSS-III Baryon Oscillation Spectroscopic Survey data release 12 : galaxy target selection and large-scale structure catalogues

The Baryon Oscillation Spectroscopic Survey (BOSS), part of the Sloan Digital Sky Survey (SDSS) III project, has provided the largest survey of galaxy redshifts available to date, in terms of both the number of galaxy redshifts measured by a single survey, and the effective cosmological volume covered. Key to analysing the clustering of these data to provide cosmological measurements is understanding the detailed properties of this sample. Potential issues include variations in the target catalogue caused by changes either in the targeting algorithm or properties of the data used, the pattern of spectroscopic observations, the spatial distribution of targets for which redshifts were not obtained, and variations in the target sky density due to observational systematics. We document here the target selection algorithms used to create the galaxy samples that comprise BOSS. We also present the algorithms used to create large-scale structure catalogues for the final Data Release (DR12) samples and the associated random catalogues that quantify the survey mask. The algorithms are an evolution of those used by the BOSS team to construct catalogues from earlier data, and have been designed to accurately quantify the galaxy sample. The code used, designated mksample, is released with this paper.Publisher PDFPeer reviewe

Eight-Dimensional Mid-Infrared/Optical Bayesian Quasar Selection

We explore the multidimensional, multiwavelength selection of quasars from mid-IR (MIR) plus optical data, specifically from Spitzer-IRAC and the Sloan Digital Sky Survey (SDSS). We apply modern statistical techniques to combined Spitzer MIR and SDSS optical data, allowing up to 8-D color selection of quasars. Using a Bayesian selection method, we catalog 5546 quasar candidates to an 8.0 um depth of 56 uJy over an area of ~24 sq. deg; ~70% of these candidates are not identified by applying the same Bayesian algorithm to 4-color SDSS optical data alone. Our selection recovers 97.7% of known type 1 quasars in this area and greatly improves the effectiveness of identifying 3.5<z<5 quasars. Even using only the two shortest wavelength IRAC bandpasses, it is possible to use our Bayesian techniques to select quasars with 97% completeness and as little as 10% contamination. This sample has a photometric redshift accuracy of 93.6% (Delta Z +/-0.3), remaining roughly constant when the two reddest MIR bands are excluded. While our methods are designed to find type 1 (unobscured) quasars, as many as 1200 of the objects are type 2 (obscured) quasar candidates. Coupling deep optical imaging data with deep mid-IR data could enable selection of quasars in significant numbers past the peak of the quasar luminosity function (QLF) to at least z~4. Such a sample would constrain the shape of the QLF and enable quasar clustering studies over the largest range of redshift and luminosity to date, yielding significant gains in our understanding of quasars and the evolution of galaxies.Comment: 49 pages, 14 figures, 7 tables. AJ, accepte

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey : baryon acoustic oscillations in the Data Releases 10 and 11 Galaxy samples

We present a one per cent measurement of the cosmic distance scale from the detections of the baryon acoustic oscillations (BAO) in the clustering of galaxies from the Baryon Oscillation Spectroscopic Survey, which is part of the Sloan Digital Sky Survey III. Our results come from the Data Release 11 (DR11) sample, containing nearly one million galaxies and covering approximately 8500 square degrees and the redshift range 0.2 < z < 0.7. We also compare these results with those from the publicly released DR9 and DR10 samples. Assuming a concordance Λ cold dark matter (ΛCDM) cosmological model, the DR11 sample covers a volume of 13 Gpc3 and is the largest region of the Universe ever surveyed at this density. We measure the correlation function and power spectrum, including density-field reconstruction of the BAO feature. The acoustic features are detected at a significance of over 7σ in both the correlation function and power spectrum. Fitting for the position of the acoustic features measures the distance relative to the sound horizon at the drag epoch, rd, which has a value of rd,fid = 149.28 Mpc in our fiducial cosmology. We find DV = (1264 ± 25 Mpc)(rd/rd,fid) at z = 0.32 and DV = (2056 ± 20 Mpc)(rd/rd,fid) at z = 0.57. At 1.0 per cent, this latter measure is the most precise distance constraint ever obtained from a galaxy survey. Separating the clustering along and transverse to the line of sight yields measurements at z = 0.57 of DA = (1421 ± 20 Mpc)(rd/rd,fid) and H = (96.8 ± 3.4 km s−1 Mpc−1)(rd,fid/rd). Our measurements of the distance scale are in good agreement with previous BAO measurements and with the predictions from cosmic microwave background data for a spatially flat CDM model with a cosmological constant.Publisher PDFPeer reviewe

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Analysis of potential systematics

We analyze the density field of galaxies observed by the Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) included in the SDSS Data Release Nine (DR9). DR9 includes spectroscopic redshifts for over 400,000 galaxies spread over a footprint of 3,275 deg^2. We identify, characterize, and mitigate the impact of sources of systematic uncertainty on large-scale clustering measurements, both for angular moments of the redshift-space correlation function and the spherically averaged power spectrum, P(k), in order to ensure that robust cosmological constraints will be obtained from these data. A correlation between the projected density of stars and the higher redshift (0.43 < z < 0.7) galaxy sample (the `CMASS' sample) due to imaging systematics imparts a systematic error that is larger than the statistical error of the clustering measurements at scales s > 120h^-1Mpc or k < 0.01hMpc^-1. We find that these errors can be ameliorated by weighting galaxies based on their surface brightness and the local stellar density. We use mock galaxy catalogs that simulate the CMASS selection function to determine that randomly selecting galaxy redshifts in order to simulate the radial selection function of a random sample imparts the least systematic error on correlation function measurements and that this systematic error is negligible for the spherically averaged correlation function. The methods we recommend for the calculation of clustering measurements using the CMASS sample are adopted in companion papers that locate the position of the baryon acoustic oscillation feature (Anderson et al. 2012), constrain cosmological models using the full shape of the correlation function (Sanchez et al. 2012), and measure the rate of structure growth (Reid et al. 2012). (abridged)Comment: Matches version accepted by MNRAS. Clarifications and references have been added. See companion papers that share the "The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey:" titl