Deblending and classifying astronomical sources with Mask R-CNN deep learning

Colin J. Burke, Patrick D. Aleo, Yu Ching Chen, Xin Liu, John R. Peterson, Glenn H. Sembroski, Joshua Yao Yu Lin

Research output: Contribution to journalArticlepeer-review


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 rcnn.

Original languageEnglish (US)
Pages (from-to)3952-3965
Number of pages14
JournalMonthly Notices of the Royal Astronomical Society
Issue number3
StatePublished - Dec 1 2019


  • Galaxies: general
  • Methods: data analysis
  • Techniques: image processing

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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