Star-galaxy classification using deep convolutional neural networks

Edward J. Kim, Robert J. Brunner

Research output: Contribution to journalArticlepeer-review


Most existing star-galaxy classifiers use the reduced summary information from catalogues, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks (ConvNets) allow a machine to automatically learn the features directly from the data, minimizing the need for input from human experts. We present a star-galaxy classification framework that uses deep ConvNets directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey and the Canada-France-Hawaii Telescope Lensing Survey, we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey and the Large Synoptic Survey Telescope, because deep neural networks require very little, manual feature engineering.

Original languageEnglish (US)
Pages (from-to)4463-4475
Number of pages13
JournalMonthly Notices of the Royal Astronomical Society
Issue number4
StatePublished - Feb 1 2017


  • Galaxies: statistics
  • Methods: data analysis
  • Methods: statistical
  • Stars: statistics
  • Surveys
  • Techniques: image processing

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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