The Dark Energy Survey Image Processing Pipeline

Morganson, E.; Gruendl, R. A.; Menanteau, F.; Kind, M. Carrasco; Chen, Y. -C.; Daues, G.; Drlica-Wagner, A.; Friedel, D. N.; Gower, M.; Johnson, M. W.  G.; Johnson, M. D.; Kessler, R.; Paz-Chinchón, F.; Petravick, D.; Pond, C.; Yanny, B.; Allam, S.; Armstrong, R.; Barkhouse, W.; Bechtol, K.; Benoit-Lévy, A.; Bernstein, G. M.; Bertin, E.; Buckley-Geer, E.; Covarrubias, R.; Desai, S.; Diehl, H. T.; Goldstein, D. A.; Gruen, D.; Li, T. S.; Lin, H.; Marriner, J.; Mohr, J. J.; Neilsen, E.; Ngeow, C. -C.; Paech, K.; Rykoff, E. S.; Sako, M.; Sevilla-Noarbe, I.; Sheldon, E.; Sobreira, F.; Tucker, D. L.; Wester, W.

doi:10.1088/1538-3873/aab4ef

Title: The Dark Energy Survey Image Processing Pipeline

Journal Article · Thu May 17 00:00:00 EDT 2018 · Publications of the Astronomical Society of the Pacific

DOI:https://doi.org/10.1088/1538-3873/aab4ef· OSTI ID:1457775

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

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. In this paper, 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.

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Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), High Energy Physics (HEP)

Contributing Organization:: DES Collaboration

Grant/Contract Number:: AC02-76SF00515; AC02-07CH11359

OSTI ID:: 1457775

Journal Information:: Publications of the Astronomical Society of the Pacific, Vol. 130, Issue 989; ISSN 0004-6280

Publisher:: Astronomical Society of the PacificCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 151 works

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Finding high-redshift strong lenses in DES using convolutional neural networks Jacobs, C.; Collett, T.; Glazebrook, K. arXiv https://doi.org/10.48550/arxiv.1811.03786	text	January 2018
The precious set of radio-optical reference frame objects in the light of Gaia DR2 data Makarov, Valeri V.; Berghea, Ciprian T.; Frouard, Julien arXiv https://doi.org/10.48550/arxiv.1811.10117	text	January 2018
Astrometry and Occultation predictions to Trans-Neptunian and Centaur Objects observed within the Dark Energy Survey Banda-Huarca, Martin; Camargo, Julio; Desmars, Josselin arXiv https://doi.org/10.48550/arxiv.1811.10724	text	January 2018
Dark Energy Survey Year 1 results: Detection of Intra-cluster Light at Redshift $\sim$ 0.25 Zhang, Y.; Yanny, B.; Palmese, A. arXiv https://doi.org/10.48550/arxiv.1812.04004	text	January 2018
Superluminous Supernovae from the Dark Energy Survey Angus, C. R.; Smith, M.; Sullivan, M. arXiv https://doi.org/10.48550/arxiv.1812.04071	text	January 2018
CIV Black Hole Mass Measurements with the Australian Dark Energy Survey (OzDES) Hoormann, J. K.; Martini, P.; Davis, T. M. arXiv https://doi.org/10.48550/arxiv.1902.04206	text	January 2019
Rediscovery of the Sixth Star Cluster in the Fornax Dwarf Spheroidal Galaxy Wang, Mei-Yu; Koposov, Sergey; Drlica-Wagner, Alex arXiv https://doi.org/10.48550/arxiv.1902.04589	text	January 2019
Galaxy shape measurement with convolutional neural networks Ribli, Dezső; Dobos, László; Csabai, István arXiv https://doi.org/10.48550/arxiv.1902.08161	text	January 2019
HI galaxies with little star formation: an abundance of LIERs Parkash, Vaishali; Brown, Michael J. I.; Jarrett, T. H. arXiv https://doi.org/10.48550/arxiv.1903.02024	text	January 2019
Identification of RR Lyrae stars in multiband, sparsely-sampled data from the Dark Energy Survey using template fitting and Random Forest classification Stringer, K. M.; Long, J. P.; Macri, L. M. arXiv https://doi.org/10.48550/arxiv.1905.00428	text	January 2019