skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Crowdsourcing quality control for Dark Energy Survey images

Journal Article · · Astronomy and Computing
ORCiD logo [1];  [2]; ORCiD logo [3];  [4];  [5];  [6];  [3];  [7];  [8];  [3];  [9];  [10];  [11];  [12];  [13];  [9];  [14];  [8];  [15];  [3] more »;  [3];  [16];  [12];  [15];  [17];  [18];  [1];  [5];  [19];  [20];  [21];  [9];  [19];  [21];  [3];  [9];  [22];  [23];  [24];  [3];  [25];  [5];  [3];  [19];  [26];  [15];  [27];  [5];  [3];  [15] « less
  1. The Ohio State Univ., Columbus, OH (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
  3. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  4. Stanford Univ., CA (United States). Kavli Inst.; SLAC National Accelerator Lab., Menlo Park, CA (United States)
  5. National Optical Astronomy Observatory, La Serena (Chile)
  6. Univ. College London, London (United Kingdom); Rhodes Univ., Grahamstown (South Aftrica)
  7. Centre National de la Recherche Scientifique (CNRS), Paris (France); Univ. College London, London (United Kingdom); Sorbonne Univ., Paris (France)
  8. Univ. College London, London (United Kingdom)
  9. Inter-Institutional e-Astronomy Lab (LIneA), Rio de Janeiro (Brazil); National Observatory, Rio de Janeiro (Brazil)
  10. Univ. of Illinois, Urbana, IL (United States); National Center for Supercomputing Applications, Urbana, IL (United States)
  11. Inst. of Space Sciences and Higher Council for Scientific Research, Barcelona (Spain); The Barcelona Institute of Science and Technology (BIST), Barcelona (Spain). Inst. of High Energy Physics
  12. Inst. of Space Sciences and Higher Council for Scientific Research, Barcelona (Spain)
  13. Univ. of Portsmouth (United Kingdom); Univ. of Southampton (United Kingdom)
  14. Excellence Cluster Universe, Garching (Germany); Ludwig-Maximilians Univ., Munich, (Germany)
  15. Univ. of Michigan, Ann Arbor, MI (United States)
  16. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Univ. of Chicago, IL (United States). Kavli Inst. for Cosmological Physics (KICP)
  17. Max Planck Inst. fur Extraterrestrische Physik, Garching (Germany); Univ. Sternwarte and Univ. Ludwig-Maximilians, Munchen (Germany)
  18. Univ. of Illinois, Urbana, IL (United States); National Center for Supercomputing Applications, Urbana, IL (United States);
  19. Univ. of Pennsylvania, Philadelphia, PA (United States)
  20. Australian Astronomical Observatory, North Ryde, NSW (Australia)
  21. Texas A&M Univ., College Station, TX (United States)
  22. California Inst. of Technology (CalTech), La Canada Flintridge, CA (United States). Jet Propulsion Lab.
  23. Univ. of Sussex, Brighton (United Kingdom)
  24. Center for Environmental and Technological Energy Research (CIEMAT), Madrid (Spain)
  25. Center for Environmental and Technological Energy Research (CIEMAT), Madrid (Spain); Univ. of Illinois, Urbana, IL (United States)
  26. National Center for Supercomputing Applications, Urbana, IL (United States)
  27. Argonne National Lab. (ANL), Argonne, IL (United States)
+ Show Author Affiliations

We have developed here a crowdsourcing web application for image quality control employed by the Dark Energy Survey. Dubbed the “DES exposure checker”, it renders science-grade images directly to a web browser and allows users to mark problematic features from a set of predefined classes. Users can also generate custom labels and thus help identify previously unknown problem classes. User reports are fed back to hardware and software experts to help mitigate and eliminate recognized issues. We report on the implementation of the application and our experience with its over 100 users, the majority of which are professional or prospective astronomers but not data management experts. We discuss aspects of user training and engagement, and demonstrate how problem reports have been pivotal to rapidly correct artifacts which would likely have been too subtle or infrequent to be recognized otherwise. Finally, we conclude with a number of important lessons learned, suggest possible improvements, and recommend this collective exploratory approach for future astronomical surveys or other extensive data sets with a su ciently large user base.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States); Argonne National Laboratory (ANL), Argonne, IL (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); The Ohio State Univ., Columbus, OH (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP); National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES); Science and Technology Facilities Council; University of Illinois-Urbana-Champaign - National Center for Supercomputing Applications; University of Chicago - Kavli Institute for Cosmological Physics
Contributing Organization:
DES
Grant/Contract Number:
SC00112704; FG02-91ER40690; AC02-98CH10886; PHYS-1066293; AST-1138766; AYA2012-39559; ESP2013-48274; FPA2013-47986; SEV-2012-0234; AC02-07CH11359; AC02-06CH11357; SC0011726
OSTI ID:
1336176
Alternate ID(s):
OSTI ID: 1275505; OSTI ID: 1346698; OSTI ID: 1356682; OSTI ID: 1602511
Report Number(s):
BNL-113170-2016-JA; arXiv:1511.03391; FERMILAB-PUB-15-487; KA2301020
Journal Information:
Astronomy and Computing, Vol. 16, Issue C; ISSN 2213-1337
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 9 works
Citation information provided by
Web of Science

References (11)

SExtractor: Software for source extraction journal June 1996
The LSC glitch group: monitoring noise transients during the fifth LIGO science run journal September 2008
The Blanco Cosmology Survey: data Acquisition, Processing, Calibration, Quality Diagnostics, and data Release journal September 2012
Use of the Hough transformation to detect lines and curves in pictures journal January 1972
The dark Energy Camera journal October 2015
Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey ★ journal September 2008
Crowdsourcing, the great meteor storm of 1833, and the founding of meteor science journal June 2014
A Modified Magnitude System that Produces Well-Behaved Magnitudes, Colors, and Errors Even for Low Signal-to-Noise Ratio Measurements journal September 1999
Space Warps – I. Crowdsourcing the discovery of gravitational lenses journal November 2015
A Review of Techniques for Extracting Linear Features from Imagery journal December 2004
The Sloan Digital Sky Survey: Technical Summary journal September 2000

Cited By (7)

Dark Energy Survey Year 1 Results: The Photometric Data Set for Cosmology journal April 2018
Comparative performance of selected variability detection techniques in photometric time series data journal September 2016
A transient search using combined human and machine classifications journal July 2017
MAXIMASK and MAXITRACK: Two new tools for identifying contaminants in astronomical images using convolutional neural networks journal February 2020
Variability search in M 31 using principal component analysis and the Hubble Source Catalogue journal March 2018
Dark Energy Survey Year 1 Results: Photometric Data Set for Cosmology text January 2018
Dark Energy Survey Year 1 Results: Photometric Data Set for Cosmology text January 2017

Similar Records

Cosmic voids and void lensing in the Dark Energy Survey Science Verification data
Journal Article · Wed Oct 26 00:00:00 EDT 2016 · Monthly Notices of the Royal Astronomical Society · OSTI ID:1336176
+69 more
VDES J2325-5229 a z = 2.7 gravitationally lensed quasar discovered using morphology-independent supervised machine learning
Journal Article · Thu Nov 17 00:00:00 EST 2016 · Monthly Notices of the Royal Astronomical Society · OSTI ID:1336176
+56 more
Joint analysis of galaxy-galaxy lensing and galaxy clustering: Methodology and forecasts for Dark Energy Survey
Journal Article · Fri Sep 30 00:00:00 EDT 2016 · Physical Review D · OSTI ID:1336176
+68 more

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
Surveys
Information systems Crowdsourcing
Human-centered computing Collaborative filtering
97 MATHEMATICS AND COMPUTING
Human-centered computing: Collaborative filtering
Information systems: Crowdsourcing
surveys
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS