Morphological galaxy classification with shapelets

René Andrae, Peter Melchior

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


We present an unsupervised classification algorithm, that identifies natural classes of galaxy morphologies. Working on SDSS G-band imaging data, we encode the morphologies by shapelet decomposition. The algorithm employs a model-based soft clustering analysis to find groupings of similar data points. We demonstrate that the algorithm is able to clearly identify and distinguish groups of elliptical, face-on and edge-on spiral galaxies in a training data set. Based on the soft clustering results, we set up a soft classifier for a data set containing 1602 SDSS galaxies.

Original languageEnglish (US)
Pages (from-to)129-133
Number of pages5
JournalAIP Conference Proceedings
StatePublished - Dec 1 2008
Externally publishedYes
EventClassification and Discovery in Large Astronomical Surveys - Ringberg Castle, Germany
Duration: Oct 14 2008Oct 17 2008

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


  • Galaxies
  • Morphology
  • SDSS
  • Shapelets
  • Soft clustering
  • Unsupervised learning


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