The Dark Energy Survey Image Processing Pipeline
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. 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.
- Authors:
- more »
- Publication Date:
- Research Org.:
- SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Contributing Org.:
- DES Collaboration
- OSTI Identifier:
- 1457775
- Grant/Contract Number:
- AC02-76SF00515; AC02-07CH11359
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Publications of the Astronomical Society of the Pacific
- Additional Journal Information:
- Journal Volume: 130; Journal Issue: 989; Journal ID: ISSN 0004-6280
- Publisher:
- Astronomical Society of the Pacific
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS
Citation Formats
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., and Wester, W. The Dark Energy Survey Image Processing Pipeline. United States: N. p., 2018.
Web. doi:10.1088/1538-3873/aab4ef.
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. The Dark Energy Survey Image Processing Pipeline. United States. https://doi.org/10.1088/1538-3873/aab4ef
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., and Wester, W. Thu .
"The Dark Energy Survey Image Processing Pipeline". United States. https://doi.org/10.1088/1538-3873/aab4ef. https://www.osti.gov/servlets/purl/1457775.
@article{osti_1457775,
title = {The Dark Energy Survey Image Processing Pipeline},
author = {Morganson, E. and Gruendl, R. A. and Menanteau, F. and Kind, M. Carrasco and Chen, Y. -C. and Daues, G. and Drlica-Wagner, A. and Friedel, D. N. and Gower, M. and Johnson, M. W. G. and Johnson, M. D. and Kessler, R. and Paz-Chinchón, F. and Petravick, D. and Pond, C. and Yanny, B. and Allam, S. and Armstrong, R. and Barkhouse, W. and Bechtol, K. and Benoit-Lévy, A. and Bernstein, G. M. and Bertin, E. and Buckley-Geer, E. and Covarrubias, R. and Desai, S. and Diehl, H. T. and Goldstein, D. A. and Gruen, D. and Li, T. S. and Lin, H. and Marriner, J. and Mohr, J. J. and Neilsen, E. and Ngeow, C. -C. and Paech, K. and Rykoff, E. S. and Sako, M. and Sevilla-Noarbe, I. and Sheldon, E. and Sobreira, F. and Tucker, D. L. and Wester, W.},
abstractNote = {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.},
doi = {10.1088/1538-3873/aab4ef},
journal = {Publications of the Astronomical Society of the Pacific},
number = 989,
volume = 130,
place = {United States},
year = {Thu May 17 00:00:00 EDT 2018},
month = {Thu May 17 00:00:00 EDT 2018}
}
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Figures / Tables:
Figure 1: The DES g, r, i, z and Y Standard Bandpasses. These curves include atmospheric transmission and instrumental response. Details can be found in Burke et al. (2018).
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The precious set of radio-optical reference frame objects in the light of Gaia DR2 data
text, January 2018
- Makarov, Valeri V.; Berghea, Ciprian T.; Frouard, Julien
- arXiv
Astrometry and Occultation predictions to Trans-Neptunian and Centaur Objects observed within the Dark Energy Survey
text, January 2018
- Banda-Huarca, Martin; Camargo, Julio; Desmars, Josselin
- arXiv
Dark Energy Survey Year 1 results: Detection of Intra-cluster Light at Redshift $\sim$ 0.25
text, January 2018
- Zhang, Y.; Yanny, B.; Palmese, A.
- arXiv
Superluminous Supernovae from the Dark Energy Survey
text, January 2018
- Angus, C. R.; Smith, M.; Sullivan, M.
- arXiv
CIV Black Hole Mass Measurements with the Australian Dark Energy Survey (OzDES)
text, January 2019
- Hoormann, J. K.; Martini, P.; Davis, T. M.
- arXiv
Rediscovery of the Sixth Star Cluster in the Fornax Dwarf Spheroidal Galaxy
text, January 2019
- Wang, Mei-Yu; Koposov, Sergey; Drlica-Wagner, Alex
- arXiv
Galaxy shape measurement with convolutional neural networks
text, January 2019
- Ribli, Dezső; Dobos, László; Csabai, István
- arXiv
HI galaxies with little star formation: an abundance of LIERs
text, January 2019
- Parkash, Vaishali; Brown, Michael J. I.; Jarrett, T. H.
- arXiv
Identification of RR Lyrae stars in multiband, sparsely-sampled data from the Dark Energy Survey using template fitting and Random Forest classification
text, January 2019
- Stringer, K. M.; Long, J. P.; Macri, L. M.
- arXiv
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