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1. The Dark Energy Survey Data Release 1

Author

M. W. G. Johnson, M. Baumer, Tesla E. Jeltema, Eric H. Neilsen, Peter Nugent, Martin Crocce, S. Hamilton, M. Smith, Antonella Palmese, Nora Shipp, Eli S. Rykoff, F. Nogueira, L. Baruah, Daniel Gruen, Daniel Scolnic, Brian Nord, M. A. G. Maia, Brian Yanny, Matthias Klein, J. Song, R. R. Gupta, Yanxi Zhang, Arya Farahi, J. Carretero, M. March, Basilio X. Santiago, Tamara M. Davis, Shantanu Desai, Gary S. Da Costa, Jochen Weller, T. F. Eifler, Daniel A. Goldstein, J. M. Hislop, Joseph J. Mohr, R. C. Thomas, Erin Sheldon, David Brooks, M. E. C. Swanson, A. Porredon, A. Carnero Rosell, A. Saro, D. W. Gerdes, Xi Chen, Attila Kovács, Eric Morganson, R. C. Wolf, J. P. Dietrich, A. Kremin, T. M. C. Abbott, Richard G. McMahon, Jeremy Mould, J. D. Maloney, Jacobo Asorey, A. Benoit-Lévy, Hiranya V. Peiris, Ofer Lahav, Vinu Vikram, J. Lasker, E. J. Sanchez, B. Flaugher, S. Juneau, Risa H. Wechsler, Ricardo L. C. Ogando, Alistair R. Walker, A. A. Plazas, Ryan J. Foley, M. Carrasco Kind, W. C. Wester, Jennifer L. Marshall, D. L. Burke, Adam Amara, A. Scott, M.L. Sánchez, Jonathan Blazek, C. B. D'Andrea, Marcelle Soares-Santos, R. C. Smith, S. E. Kuhlmann, Ashley J. Ross, Robert C. Nichol, Ben Hoyle, G. Daues, M. Gower, C. J. Miller, M. D. Johnson, Wayne A. Barkhouse, Samuel Hinton, Felipe Menanteau, Kevin Reil, L. Nunes, F. Paz-Chinchón, David J. James, Tenglin Li, Scott Dodelson, Santiago Avila, Ann Elliott, Chihway Chang, T. Kacprzak, G. Tarle, Knut Olsen, R. Das, Ramon Miquel, Lyndsay Old, Juan Garcia-Bellido, E. Bertin, A. Roodman, Tommaso Giannantonio, Carlos E. Cunha, J. Poh, Pablo Fosalba, Enrique Gaztanaga, G. Gutierrez, J. DeRose, J. J. Thaler, Enrique Fernández, Will J. Percival, S. Allam, Paul M. Ricker, A. Pujol, Robert A. Gruendl, V. Scarpine, Andrew B. Pace, R. P. Rollins, K. Honscheid, Timothy A. McKay, Darren L. DePoy, G. Khullar, R. T. Li, Joe Zuntz, Alex Drlica-Wagner, R. Nikutta, Francisco J. Castander, C. Pond, Douglas L. Tucker, Don Petravick, W. G. Hartley, A. K. Vivas, R. Cawthon, Riccardo Campisano, D. A. Finley, D. Brout, Karl Glazebrook, Dragan Huterer, Peter Melchior, Elisabeth Krause, Mark Sullivan, Kyler Kuehn, V. C. Busti, P. Rooney, C. J. Conselice, Huan Lin, Marc Manera, J. Annis, Sebastian Bocquet, A. M. G. Koziol, M. L. Silveira, M. Fitzpatrick, Andrew R. Liddle, Alfredo Zenteno, O. Ballester, Steve Kent, Daniel Thomas, E. Buckley-Geer, Michael Troxel, A. K. Romer, Paul Martini, A. Fausti Neto, Keith Bechtol, C. Davis, J. Gschwend, Gary Bernstein, Hao-Yi Wu, Peter Doel, H. T. Diehl, J. De Vicente, J. P. Marriner, M. S. S. Gill, E. Suchyta, Niall MacCrann, August E. Evrard, Alexandre Refregier, Douglas N. Friedel, Albert Stebbins, M. Banerji, Joshua A. Frieman, I. Sevilla-Noarbe, J. A. Smith, T. McClintock, N. Kuropatkin, L. N. da Costa, Laia Cardiel-Sas, M. Sako, D. L. Hollowood, S. Serrano, David L. Nidever, Marcos Lima, Richard G. Kron, P. Lopez-Reyes, Filipe B. Abdalla, Bhuvnesh Jain, Matthew R. Becker, Flavia Sobreira, Institut d'Astrophysique de Paris ( IAP ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), DES, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), NOAO Data Lab, Abbott, T. M. C., Abdalla, F. B., Allam, S., Amara, A., Annis, J., Asorey, J., Avila, S., Ballester, O., Banerji, M., Barkhouse, W., Baruah, L., Baumer, M., Bechtol, K., Becker, M. R., Benoit-Lévy, A., Bernstein, G. M., Bertin, E., Blazek, J., Bocquet, S., Brooks, D., Brout, Buckley-Geer, E., Burke, D. L., Busti, V., Campisano, R., Cardiel-Sas, L., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., Castander, F. J., Cawthon, R., Chang, C., Chen, X., Conselice, C., Costa, G., Crocce, M., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Das, R., Daues, G., Davis, T. M., Davis, C., De Vicente, J., Depoy, D. L., Derose, J., Desai, S., Diehl, H. T., Dietrich, J. P., Dodelson, S., Doel, P., Drlica-Wagner, A., Eifler, T. F., Elliott, A. E., Evrard, A. E., Farahi, A., Fausti Neto, A., Fernandez, E., Finley, D. A., Flaugher, B., Foley, R. J., Fosalba, P., Friedel, D. N., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Giannantonio, T., Gill, M. S. S., Glazebrook, K., Goldstein, D. A., Gower, M., Gruen, D., Gruendl, R. A., Gschwend, J., Gupta, R. R., Gutierrez, G., Hamilton, S., Hartley, W. G., Hinton, S. R., Hislop, J. M., Hollowood, D., Honscheid, K., Hoyle, B., Huterer, D., Jain, B., James, D. J., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Kacprzak, T., Kent, S., Khullar, G., Klein, M., Kovacs, A., Koziol, A. M. G., Krause, E., Kremin, A., Kron, R., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Lahav, O., Lasker, J., Li, T. S., Li, R. T., Liddle, A. R., Lima, M., Lin, H., López-Reyes, P., Maccrann, N., Maia, M. A. G., Maloney, J. D., Manera, M., March, M., Marriner, J., Marshall, J. L., Martini, P., Mcclintock, T., Mckay, T., Mcmahon, R. G., Melchior, P., Menanteau, F., Miller, C. J., Miquel, R., Mohr, J. J., Morganson, E., Mould, J., Neilsen, E., Nichol, R. C., Nogueira, F., Nord, B., Nugent, P., Nunes, L., Ogando, R. L. C., Old, L., Pace, A. B., Palmese, A., Paz-Chinchón, F., Peiris, H. V., Percival, W. J., Petravick, D., Plazas, A. A., Poh, J., Pond, C., Porredon, A., Pujol, A., Refregier, A., Reil, K., Ricker, P. M., Rollins, R. P., Romer, A. K., Roodman, A., Rooney, P., Ross, A. J., Rykoff, E. S., Sako, M., Sanchez, M. L., Sanchez, E., Santiago, B., Saro, A., Scarpine, V., Scolnic, D., Serrano, S., Sevilla-Noarbe, I., Sheldon, E., Shipp, N., Silveira, M. L., Smith, M., Smith, R. C., Smith, J. A., Soares-Santos, M., Sobreira, F., Song, J., Stebbins, A., Suchyta, E., Sullivan, M., Swanson, M. E. C., Tarle, G., Thaler, J., Thomas, D., Thomas, R. C., Troxel, M. A., Tucker, D. L., Vikram, V., Vivas, A. K., Walker, A. R., Wechsler, R. H., Weller, J., Wester, W., Wolf, R. C., Wu, H., Yanny, B., Zenteno, A., Zhang, Y., Zuntz, J., Des, Collaboration, Juneau, S., Fitzpatrick, M., Nikutta, R., Nidever, D., Olsen, K., Scott, A., and NOAO Data, Lab

Subjects
Astronomical Objects, catalog, [ PHYS.ASTR ] Physics [physics]/Astrophysics [astro-ph], techniques: image processing, Astrophysics, astronomical databases: miscellaneous, 01 natural sciences, law.invention, photometric [techniques], techniques: photometric, law, Observatory, Astrophysics - Cosmology and Nongalactic Astrophysic, 010303 astronomy & astrophysics, [ PHYS.PHYS.PHYS-INS-DET ] Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Physics, image processing [techniques], observations [cosmology], sky survey, Astrophysics - Solar and Stellar Astrophysics, astro-ph.CO, Astrophysics - Instrumentation and Methods for Astrophysics, sextractor, Data release, observation [cosmology], Astrophysics - Cosmology and Nongalactic Astrophysics, astro-ph.SR, Cosmology and Nongalactic Astrophysics (astro-ph.CO), Aperture, astro-ph.GA, FOS: Physical sciences, Astronomy & Astrophysics, miscellaneou [astronomical databases], Telescope, surveys, Astrophysics - Astrophysics of Galaxie, 0103 physical sciences, survey, Astronomical And Space Sciences, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Instrumentation and Methods for Astrophysics (astro-ph.IM), Solar and Stellar Astrophysics (astro-ph.SR), Physical Chemistry (Incl. Structural), 010308 nuclear & particles physics, Organic Chemistry, representations, Astronomy and Astrophysics, miscellaneous [astronomical databases], angular masks, Astrophysics - Astrophysics of Galaxies, Galaxy, Data set, catalogs, cosmology: observations, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), Dark energy, Astrophysics - Instrumentation and Methods for Astrophysic, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], astro-ph.IM
Abstract

We describe the first public data release of the Dark Energy Survey, DES DR1, consisting of reduced single epoch images, coadded images, coadded source catalogs, and associated products and services assembled over the first three years of DES science operations. DES DR1 is based on optical/near-infrared imaging from 345 distinct nights (August 2013 to February 2016) by the Dark Energy Camera mounted on the 4-m Blanco telescope at Cerro Tololo Inter-American Observatory in Chile. We release data from the DES wide-area survey covering ~5,000 sq. deg. of the southern Galactic cap in five broad photometric bands, grizY. DES DR1 has a median delivered point-spread function of g = 1.12, r = 0.96, i = 0.88, z = 0.84, and Y = 0.90 arcsec FWHM, a photometric precision of < 1% in all bands, and an astrometric precision of 151 mas. The median coadded catalog depth for a 1.95" diameter aperture at S/N = 10 is g = 24.33, r = 24.08, i = 23.44, z = 22.69, and Y = 21.44 mag. DES DR1 includes nearly 400M distinct astronomical objects detected in ~10,000 coadd tiles of size 0.534 sq. deg. produced from ~39,000 individual exposures. Benchmark galaxy and stellar samples contain ~310M and ~ 80M objects, respectively, following a basic object quality selection. These data are accessible through a range of interfaces, including query web clients, image cutout servers, jupyter notebooks, and an interactive coadd image visualization tool. DES DR1 constitutes the largest photometric data set to date at the achieved depth and photometric precision., 30 pages, 20 Figures. Release page found at this url https://des.ncsa.illinois.edu/releases/dr1

Published
2018
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2. The Dark Energy Survey Image Processing Pipeline

Author

Eric H. Neilsen, Felipe Menanteau, Gary Bernstein, M. Carrasco Kind, Brian Yanny, Shantanu Desai, Daniel A. Goldstein, Joseph J. Mohr, Eli S. Rykoff, E. Bertin, W. C. Wester, H. T. Diehl, Flavia Sobreira, J. P. Marriner, Don Petravick, Erin Sheldon, A. Benoit-Lévy, M. W. G. Johnson, G. Daues, Douglas L. Tucker, Douglas N. Friedel, Richard Kessler, T. S. Li, I. Sevilla-Noarbe, Y.-C. Chen, E. Buckley-Geer, M. Sako, C. Pond, Chow-Choong Ngeow, R. Covarrubias, Daniel Gruen, M. D. Johnson, Wayne A. Barkhouse, Eric Morganson, M. Gower, F. Paz-Chinchón, Robert Armstrong, K. Paech, Keith Bechtol, S. Allam, Robert A. Gruendl, Alex Drlica-Wagner, Huan Lin, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), DES, Institut d'Astrophysique de Paris ( IAP ), and Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
Image quality, Pipeline (computing), media_common.quotation_subject, FOS: Physical sciences, Image processing, Astronomical survey, 01 natural sciences, surveys, techniques - image processing, 0103 physical sciences, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Instrumentation and Methods for Astrophysics (astro-ph.IM), 010303 astronomy & astrophysics, [ PHYS.PHYS.PHYS-INS-DET ] Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], media_common, Remote sensing, COSMIC cancer database, cosmology - observations, 010308 nuclear & particles physics, techniques - photometric, Astronomy and Astrophysics, Space and Planetary Science, Sky, Magnitude (astronomy), Astrophysics - Instrumentation and Methods for Astrophysics, catalogs, Data reduction
Abstract

The Dark Energy Survey (DES) is a five-year optical imaging campaign with the goal of understanding the origin of cosmic acceleration. DES performs a similar to 5000 deg(2) survey of the southern sky in five optical bands (g, r, i, z, Y) to a depth of similar to 24th magnitude. Contemporaneously, DES performs a deep, time-domain survey in four optical bands (g, r, i, z) over similar to 27 deg(2). DES exposures are processed nightly with an evolving data reduction pipeline and evaluated for image quality to determine if they need to be retaken. Difference imaging and transient source detection are also performed in the time domain component nightly. On a bi-annual basis, DES exposures are reprocessed with a refined pipeline and coadded to maximize imaging depth. Here we describe the DES image processing pipeline in support of DES science, as a reference for users of archival DES data, and as a guide for future astronomical surveys.

Published
2018
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3. The Dark Energy Survey Data Management System

Author

Eric H. Neilsen, Luiz N. da Costa, Chow-Choong Ngeow, J. Anthony Darnell, Erin Sheldon, Wayne A. Barkhouse, Nikolay Kuropatkin, Emmanuel Bertin, Alex Parga, Ricardo L. C. Ogando, G. Daues, D. Adams, Huan Lin, M. Gower, Douglas L. Tucker, Leandro Martelli, Y. Dora Cai, Chris Stoughton, Mike Jarvis, Cristina E. Beldica, and Joseph J. Mohr

Subjects
FOS: Computer and information sciences, Computer science, Data management, media_common.quotation_subject, FOS: Physical sciences, Astrophysics, computer.software_genre, Photometry (optics), Weak gravitational lensing, media_common, Database, business.industry, Astrophysics (astro-ph), bepress|Physical Sciences and Mathematics|Computer Sciences, Galaxy, Data access, Computer Science - Distributed, Parallel, and Cluster Computing, Sky, Data quality, Management system, Computer data storage, Dark energy, Survey data collection, TeraGrid, Distributed, Parallel, and Cluster Computing (cs.DC), business, Raw data, computer
Abstract

The Dark Energy Survey collaboration will study cosmic acceleration with a 5000 deg2 griZY survey in the southern sky over 525 nights from 2011-2016. The DES data management (DESDM) system will be used to process and archive these data and the resulting science ready data products. The DESDM system consists of an integrated archive, a processing framework, an ensemble of astronomy codes and a data access framework. We are developing the DESDM system for operation in the high performance computing (HPC) environments at NCSA and Fermilab. Operating the DESDM system in an HPC environment offers both speed and flexibility. We will employ it for our regular nightly processing needs, and for more compute-intensive tasks such as large scale image coaddition campaigns, extraction of weak lensing shear from the full survey dataset, and massive seasonal reprocessing of the DES data. Data products will be available to the Collaboration and later to the public through a virtual-observatory compatible web portal. Our approach leverages investments in publicly available HPC systems, greatly reducing hardware and maintenance costs to the project, which must deploy and maintain only the storage, database platforms and orchestration and web portal nodes that are specific to DESDM. In Fall 2007, we tested the current DESDM system on both simulated and real survey data. We used Teragrid to process 10 simulated DES nights (3TB of raw data), ingesting and calibrating approximately 250 million objects into the DES Archive database. We also used DESDM to process and calibrate over 50 nights of survey data acquired with the Mosaic2 camera. Comparison to truth tables in the case of the simulated data and internal crosschecks in the case of the real data indicate that astrometric and photometric data quality is excellent., Comment: To be published in the proceedings of the SPIE conference on Astronomical Instrumentation (held in Marseille in June 2008). This preprint is made available with the permission of SPIE. Further information together with preprint containing full quality images is available at http://desweb.cosmology.uiuc.edu/wiki

Published
2008
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4. Dualit�ten und Gruppen der Ordnung p 6

Author

Hermann Heineken and G. Daues

Subjects
Pure mathematics, Differential geometry, Hyperbolic geometry, Geometry and Topology, Algebraic geometry, Topology (chemistry), Mathematics, Projective geometry
Published
1975
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5. Cyber-infrastructure to Support Science and Data Management for the Dark Energy Survey

Author

G. Daues, James Annis, D. Cai, T. Alam, Huan Lin, Raymond L. Plante, Wayne A. Barkhouse, Joseph J. Mohr, Chow-Choong Ngeow, Douglas L. Tucker, R. C. Smith, and Cristina E. Beldica

Subjects
Physics, Database, business.industry, Data management, Astrophysics (astro-ph), FOS: Physical sciences, Astrophysics, Astrometry, computer.software_genre, South Pole Telescope, Software, Middleware (distributed applications), Scalability, Raw data, Cluster analysis, business, computer
Abstract

The Dark Energy Survey (DES; operations 2009-2015) will address the nature of dark energy using four independent and complementary techniques: (1) a galaxy cluster survey over 4000 deg2 in collaboration with the South Pole Telescope Sunyaev-Zel'dovich effect mapping experiment, (2) a cosmic shear measurement over 5000 deg2, (3) a galaxy angular clustering measurement within redshift shells to redshift=1.35, and (4) distance measurements to 1900 supernovae Ia. The DES will produce 200 TB of raw data in four bands, These data will be processed into science ready images and catalogs and co-added into deeper, higher quality images and catalogs. In total, the DES dataset will exceed 1 PB, including a 100 TB catalog database that will serve as a key science analysis tool for the astronomy/cosmology community. The data rate, volume, and duration of the survey require a new type of data management (DM) system that (1) offers a high degree of automation and robustness and (2) leverages the existing high performance computing infrastructure to meet the project's DM targets. The DES DM system consists of (1) a grid-enabled, flexible and scalable middleware developed at NCSA for the broader scientific community, (2) astronomy modules that build upon community software, and (3) a DES archive to support automated processing and to serve DES catalogs and images to the collaboration and the public. In the recent DES Data Challenge 1 we deployed and tested the first version of the DES DM system, successfully reducing 700 GB of raw simulated images into 5 TB of reduced data products and cataloguing 50 million objects with calibrated astrometry and photometry., Comment: 12 pages, 3 color figures, 1 table. Published in SPIE vol. 6270

6. The Dark Energy Survey Image Processing Pipeline.

Author

E. Morganson, R. A. Gruendl, F. Menanteau, M. Carrasco Kind, Y.-C. Chen, G. Daues, A. Drlica-Wagner, D. N. Friedel, M. Gower, M. W. G. Johnson, M. D. Johnson, R. Kessler, F. Paz-Chinchón, D. Petravick, C. Pond, B. Yanny, S. Allam, R. Armstrong, W. Barkhouse, and K. Bechtol

Subjects
DARK energy, METAPHYSICAL cosmology
Abstract

The Dark Energy Survey (DES) is a five-year optical imaging campaign with the goal of understanding the origin of cosmic acceleration. DES performs a ∼5000 deg2 survey of the southern sky in five optical bands (g, r, i, z, Y) to a depth of ∼24th magnitude. Contemporaneously, DES performs a deep, time-domain survey in four optical bands (g, r, i, z) over ∼27 deg2. DES exposures are processed nightly with an evolving data reduction pipeline and evaluated for image quality to determine if they need to be retaken. Difference imaging and transient source detection are also performed in the time domain component nightly. On a bi-annual basis, DES exposures are reprocessed with a refined pipeline and coadded to maximize imaging depth. Here we describe the DES image processing pipeline in support of DES science, as a reference for users of archival DES data, and as a guide for future astronomical surveys. [ABSTRACT FROM AUTHOR]

Published
2018
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