Mining the UKIDSS GPS: star formation and embedded clusters?

  • O. Solin
  • E. Ukkonen
  • L. Haikala
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Publication date
January 2014

Abstract

Context. Data mining techniques must be developed and applied to analyse the large public data bases containing hundreds to thou-sands of millions entries. Aims. To develop methods for locating previously unknown stellar clusters from the UKIDSS Galactic Plane Survey catalogue data. Methods. The cluster candidates are computationally searched from pre-filtered catalogue data using a method that fits a mixture model of Gaussian densities and background noise using the Expectation Maximization algorithm. The catalogue data contains a significant number of false sources clustered around bright stars. A large fraction of these artefacts were automatically filtered out before or during the cluster search. The UKIDSS data reduction pipeline tends...

Extracted data

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