SDSS Imaging Pipelines

By Robert Lupton, Željko Ivezic´, James E. Gunn, G. Knapp, M. Strauss, N. Yasuda. 2001

197 citations recorded by the source corpus.

The SDSS project has taken 5-band data covering approximately 3000 deg2, or 4Tby of data. This has been processed through a set of image-processing pipelines, and the resulting catalogues of about 100 million objects have been used for a number of scientific projects. We discuss our software infrastructure, and outline the architecture of the SDSS image processing pipelines. In order to process this volume of data the pipelines have to be robust and reasonably fast; because we have been interested in looking for rare objects, the number of outliers due to deficiencies in the data and bugs in the software must be small. We have found that writing the codes has been one of the harder and more expensive aspects of the entire survey.

Subfields: Astronomy and Astrophysics, Computational Mechanics, Instrumentation, Evaluation & Benchmarks

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Citations in the corpus

Maximum inner-product search using cone trees (2012, background): “Three astronomy datasets, LCDM [27], PSF and SJ2, are also considered.”
The Size Distributions of Asteroid Families in the SDSS Moving Object Catalog 4 (2008, background): “te to better than 0.1 arcsec per coordinate (rms) for sources with r < 20.5 (Pier et al. 2003), and the morphological information from the images allows reliable stargalaxy separation to r ∼ 21.5 (Lupton et al. 2002, Scranton et al. 2002). A compendium of other technical details about SDSS can be”
WISE PHOTOMETRY FOR 400 MILLION SDSS SOURCES (2014, methodology): “These catalogs contain the outputs of the Photo pipeline (Lupton et al. 2001), and include star/galaxy classification and galaxy shape measurements using either exponential, de Vaucouleurs, or composite (sum of exponential and de Vaucouleurs) profiles.”
The Sloan Digital Sky Survey Quasar Survey: Quasar Luminosity Function from Data Release 3 (2006, methodology): “Because of the evolution of the image processing software (Lupton et al. 2002; Abazajian et al. 2003), the SDSS includes two versions of point-spread-function (PSF) photometry for each object: the TARGET photometry (using the version of the pipeline and calibration current at the time quasar target…”
GALAXY CLUSTERING IN THE COMPLETED SDSS REDSHIFT SURVEY: THE DEPENDENCE ON COLOR AND LUMINOSITY (2010, methodology): “The imaging data were processed through a series of pipelines that perform astrometric calibration (Pier et al. 2003), photometric reduction (Lupton et al. 1999, 2001) and photometric calibration (Hogg et al. 2001; Ivezić et al. 2004; Tucker et al. 2006; Padmanabhan et al. 2008).”
The Coevolution of Galaxies and Supermassive Black Holes: Insights from Surveys of the Contemporary Universe (2014, methodology): “With the advent of digital surveys of large regions of the sky, astronomers were motivated to design automated algorithms for measuring the principle properties of galaxies (e.g. Lupton et al. 2002).”
A UNIFIED CATALOG OF RADIO OBJECTS DETECTED BY NVSS, FIRST, WENSS, GB6, AND SDSS (2008, background): “The morphological information from SDSS images allows reliable star-galaxy separation to r ∼ 21.5 (Lupton et al. 2002; Scranton et al. 2002).”
Photometric Properties of Void Galaxies in the Sloan Digital Sky Survey (2003, methodology): “To compare morphological properties of void and wall galaxies, we examine the distribution of inverse concentration indices measured by the SDSS photometric pipeline (Lupton et al. 2001; Stoughton & et al. 2002; Pier et al. 2003).”
The Milky Way Tomography with SDSS. I. Stellar Number Density Distribution (2005, methodology): “Photometric errors and photomet- ric parallax dispersion (as discussed in x 4.3.3) are also mixed in.”
Celeste: Variational inference for a generative model of astronomical images (2015, methodology): “To date, the algorithms proposed for this inference problem have been primarily heuristic, based on finding bright regions in the images (Lupton et al., 2001; Stoughton et al., 2002).”
THE SLOAN DIGITAL SKY SURVEY-II SUPERNOVA SURVEY: SEARCH ALGORITHM AND FOLLOW-UP OBSERVATIONS (2007, methodology): “The raw data are first processed through the Photo pipeline (Lupton et al. 2001, 2002; Stoughton et al. 2002).”
Sloan Digital Sky Survey Standard Star Catalog for Stripe 82: The Dawn of Industrial 1% Optical Photometry (2007, methodology): “The SDSS imaging PSF is modeled heuristically in each band and each camera columnusing aKarhunen-Loéve (KL) transform (Lupton et al. 2002).”
The Milky Way Tomography with SDSS. II. Stellar Metallicity (2008, methodology): “Astrometric positions are accurate to better than 0.1 arcsec per coordinate (rms) for sources with r < 20.5 (Pier et al. 2003), and the morphological information from the images allows reliable star-galaxy separation to r ∼ 21.5 (Lupton et al. 2002; Scranton et al. 2002).”
A systematic search for changing-look quasars in SDSS (2015, methodology): “…photometric nights of good seeing (Hogg et al. 2001) and are calibrated photometrically (Smith et al. 2002; Ivezić et al. 2004a; Tucker et al. 2006; Padmanabhan et al. 2008), and astrometrically (Pier et al. 2003) before object parameters are measured (Lupton et al. 2001; Stoughton et al. 2002).”
Interaction-induced star formation in a complete sample of 105 nearby star-forming galaxies (2007, background): “More details on the photometric pipeline can be found in Lupton et al. (2001).”
Exploring the Variable Sky with the Sloan Digital Sky Survey (2007, background): “Astrometric positions are accurate to better than 0.100 per coordinate (rms) for sources with r < 20:5 (Pier et al. 2003), and the morphological information from the images allows reliable star-galaxy separation to r 21:5 (Lupton et al. 2002).”