TY - JOUR
T1 - Deblending and classifying astronomical sources with Mask R-CNN deep learning
AU - Burke, Colin J.
AU - Aleo, Patrick D.
AU - Chen, Yu Ching
AU - Liu, Xin
AU - Peterson, John R.
AU - Sembroski, Glenn H.
AU - Lin, Joshua Yao Yu
N1 - Funding Information:
Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, the Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A&M University.
Funding Information:
This project used data obtained with the Dark Energy Camera (DECam), whichwas constructedbythe DarkEnergySurvey(DES) collaboration. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundacao Carlos Chagas Filho de Amparo, Financiadora de Estudos e Projetos, Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico and the Ministerio da Ciencia, Tecnologia e Inovacao, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Ener-geticas, Medioambientales y Tecnologicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenossische Technische Hochschule (ETH) Zurich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciencies de l’Espai (IEEC/CSIC), the Institut de Fisica d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universitat Munchen and the associated Excellence
Funding Information:
This work utilizes resources supported by the National Science Foundation’s Major Research Instrumentation program, grant #1725729, as well as the University of Illinois at Urbana-Champaign. We thank Dr. Volodymyr Kindratenko and Dr. Dawei Mu at the National Center for Supercomputing Applications for their assistance with the GPU cluster used in this work. This work is based on projects started during the 2019 graduate-level AI seminar at the Department of Astronomy, University of Illinois at Urbana-Champaign. We thank the anonymous referees for helpful comments.
Publisher Copyright:
© 2019 The Author(s)
PY - 2019/12/1
Y1 - 2019/12/1
N2 - We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, ASTRO R-CNN, is publicly available at https://github.com/burke86/astro rcnn.
AB - We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, ASTRO R-CNN, is publicly available at https://github.com/burke86/astro rcnn.
KW - Galaxies: general
KW - Methods: data analysis
KW - Techniques: image processing
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U2 - 10.1093/mnras/stz2845
DO - 10.1093/mnras/stz2845
M3 - Article
AN - SCOPUS:85079601659
SN - 0035-8711
VL - 490
SP - 3952
EP - 3965
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
IS - 3
ER -