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

Abstract

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.

Authors:
 [1];  [1];  [2];  [3];  [1];  [4];  [5]
+ Show Author Affiliations
  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)
Publication Date:
Research Org.:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1234844
Report Number(s):
FERMILAB-PUB-14-578-AE
Journal ID: ISSN 0004-6280; arXiv eprint number arXiv:1409.2885
Grant/Contract Number:  
AC02-07CH11359
Resource Type:
Accepted Manuscript
Journal Name:
Publications of the Astronomical Society of the Pacific
Additional Journal Information:
Journal Volume: 127; Journal Issue: 957; 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

Zhang, Yuanyuan, McKay, Timothy A., Bertin, Emmanuel, Jeltema, Tesla, Miller, Christopher J., Rykoff, Eli, and Song, Jeeseon. Crowded Cluster Cores. Algorithms for Deblending in Dark Energy Survey Images. United States: N. p., 2015. Web. doi:10.1086/684053.
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Zhang, Yuanyuan, McKay, Timothy A., Bertin, Emmanuel, Jeltema, Tesla, Miller, Christopher J., Rykoff, Eli, & Song, Jeeseon. Crowded Cluster Cores. Algorithms for Deblending in Dark Energy Survey Images. United States. https://doi.org/10.1086/684053
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Zhang, Yuanyuan, McKay, Timothy A., Bertin, Emmanuel, Jeltema, Tesla, Miller, Christopher J., Rykoff, Eli, and Song, Jeeseon. Mon . "Crowded Cluster Cores. Algorithms for Deblending in Dark Energy Survey Images". United States. https://doi.org/10.1086/684053. https://www.osti.gov/servlets/purl/1234844.
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@article{osti_1234844,
title = {Crowded Cluster Cores. Algorithms for Deblending in Dark Energy Survey Images},
author = {Zhang, Yuanyuan and McKay, Timothy A. and Bertin, Emmanuel and Jeltema, Tesla and Miller, Christopher J. and Rykoff, Eli and Song, Jeeseon},
abstractNote = {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.},
doi = {10.1086/684053},
journal = {Publications of the Astronomical Society of the Pacific},
number = 957,
volume = 127,
place = {United States},
year = {Mon Oct 26 00:00:00 EDT 2015},
month = {Mon Oct 26 00:00:00 EDT 2015}
}
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https://doi.org/10.1086/684053
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    Works referencing / citing this record:

    ProFound: Source Extraction and Application to Modern Survey Data
    journal, February 2018

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    • Monthly Notices of the Royal Astronomical Society, Vol. 476, Issue 3
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    Deblending and classifying astronomical sources with Mask R-CNN deep learning
    journal, October 2019

    • Burke, Colin J.; Aleo, Patrick D.; Chen, Yu-Ching
    • Monthly Notices of the Royal Astronomical Society, Vol. 490, Issue 3
    • DOI: 10.1093/mnras/stz2845

    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

    • Diehl, H. T.; Buckley-Geer, E. J.; Lindgren, K. A.
    • The Astrophysical Journal Supplement Series, Vol. 232, Issue 1
    • DOI: 10.3847/1538-4365/aa8667

    Dark Energy Survey Year 1 Results: The Photometric Data Set for Cosmology
    journal, April 2018

    • Drlica-Wagner, A.; Sevilla-Noarbe, I.; Rykoff, E. S.
    • The Astrophysical Journal Supplement Series, Vol. 235, Issue 2
    • DOI: 10.3847/1538-4365/aab4f5

    Dark Energy Survey Year 1 Results: Photometric Data Set for Cosmology
    text, January 2018

    • Drlica-Wagner, A.; Sevilla-Noarbe, I.; Rykoff, Es
    • Apollo - University of Cambridge Repository
    • DOI: 10.17863/cam.21145

    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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