Title: Crowded Cluster Cores. Algorithms for Deblending in Dark Energy Survey Images

Journal Article · · Publications of the Astronomical Society of the Pacific
DOI: https://doi.org/10.1086/684053 · OSTI ID:1234844
 [1];  [1];  [2];  [3];  [1];  [4];  [5]
  1. Univ. of Michigan, Ann Arbor, MI (United States)
  2. Pierre and Marie Curie Univ., Paris (France)
  3. Univ. of California, Santa Cruz, CA (United States)
  4. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  5. Univ. of Michigan, Ann Arbor, MI (United States); Korea Astronomy and Space Science Inst., Daejeon (Korea)

Deep optical images are often crowded with overlapping objects. We found that this is especially true in the cores of galaxy clusters, where images of dozens of galaxies may lie atop one another. Accurate measurements of cluster properties require deblending algorithms designed to automatically extract a list of individual objects and decide what fraction of the light in each pixel comes from each object. In this article, we introduce a new software tool called the Gradient And Interpolation based (GAIN) deblender. GAIN is used as a secondary deblender to improve the separation of overlapping objects in galaxy cluster cores in Dark Energy Survey images. It uses image intensity gradients and an interpolation technique originally developed to correct flawed digital images. Our paper is dedicated to describing the algorithm of the GAIN deblender and its applications, but we additionally include modest tests of the software based on real Dark Energy Survey co-add images. GAIN helps to extract an unbiased photometry measurement for blended sources and improve detection completeness, while introducing few spurious detections. When applied to processed Dark Energy Survey data, GAIN serves as a useful quick fix when a high level of deblending is desired.

Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Grant/Contract Number:
AC02-07CH11359
OSTI ID:
1234844
Report Number(s):
FERMILAB-PUB-14-578-AE; arXiv eprint number arXiv:1409.2885
Journal Information:
Publications of the Astronomical Society of the Pacific, Journal Name: Publications of the Astronomical Society of the Pacific Journal Issue: 957 Vol. 127; ISSN 0004-6280
Publisher:
Astronomical Society of the PacificCopyright Statement
Country of Publication:
United States
Language:
English

References (21)

Photographic photometry in globular clusters: Comparison of techniques journal January 1983
Wide field imaging - I. Applications of neural networks to object detection and star/galaxy classification: Wide field imaging - I journal December 2000
SExtractor: Software for source extraction journal June 1996
Analysis of isoplanatic high resolution stellar fields by the StarFinder code journal December 2000
An Image Inpainting Technique Based on the Fast Marching Method journal January 2004
FOCAS - Faint Object Classification and Analysis System journal March 1981
DAOPHOT - A computer program for crowded-field stellar photometry journal March 1987
A faint-galaxy photometry and image-analysis system journal April 1991
Surface brightness and evolution of galaxies journal October 1976
Photometry of a complete sample of faint galaxies journal June 1980
The Sloan Digital Sky Survey: Technical Summary journal September 2000
Detailed Structural Decomposition of Galaxy Images journal July 2002
The DEEP Groth Strip Survey. II. Hubble Space Telescope Structural Parameters of Galaxies in the Groth Strip
  • Simard, Luc; Willmer, Christopher N. A.; Vogt, Nicole P.
  • The Astrophysical Journal Supplement Series, Vol. 142, Issue 1 https://doi.org/10.1086/341399
journal September 2002
The Fourth Data Release of the Sloan Digital Sky Survey
  • Adelman‐McCarthy, Jennifer K.; Agueros, Marcel A.; Allam, Sahar S.
  • The Astrophysical Journal Supplement Series, Vol. 162, Issue 1 https://doi.org/10.1086/497917
journal January 2006
Bayesian Methods of Astronomical Source Extraction journal June 2007
Detailed Decomposition of Galaxy Images. ii. Beyond Axisymmetric Models journal April 2010
The Blanco Cosmology Survey: data Acquisition, Processing, Calibration, Quality Diagnostics, and data Release journal September 2012
Candels Multiwavelength Catalogs: Source Identification and Photometry in the Candels Ukidss Ultra-Deep Survey Field journal May 2013
Region Filling and Object Removal by Exemplar-Based Image Inpainting journal September 2004
Systematic errors in weak lensing: application to SDSS galaxy-galaxy weak lensing journal August 2005
galapagos: from pixels to parameters: galapagos: from pixels to parameters journal March 2012

Cited By (8)

ProFound: Source Extraction and Application to Modern Survey Data journal February 2018
Deblending and classifying astronomical sources with Mask R-CNN deep learning journal October 2019
Dark Energy Survey Year 1 Results: Photometric Data Set for Cosmology text January 2018
The DES Bright Arcs Survey: Hundreds of Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey Science Verification and Year 1 Observations journal September 2017
Dark Energy Survey Year 1 Results: The Photometric Data Set for Cosmology journal April 2018
Dark Energy Survey Year 1 Results: Photometric Data Set for Cosmology text January 2017
ProFound: Source Extraction and Application to Modern Survey Data text January 2018
Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning text January 2019

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