GUASOM: Gaia Utility for Analysis and Knowledge Discovery based on Self Organizing Maps

D. Fustes, M. Manteiga, C. Dafonte, B.Arcay, , M.A. Álvarez, D.Garabato (Universidade da Coruña)

We present a method for knowledge analysis in large astronomical spectrophotometric archives. The method is based on a type of unsupervised learning Artificial Neural Networks named Self-organizing maps (SOMs). SOMs are used to organize the information in clusters of objects, as homogeneously as possible according to their spectral energy distributions, and to project them onto a 2D grid where the data structure can be visualized. Our algorithm has been tested by means of simulated Gaia spectrophotometry, which is based on SDSS observations and theoretical spectral libraries covering a wide sample of astronomical objects. We demonstrate the usefulness of the method by analyzing the spectra that were rejected by the SDSS spectroscopic classification pipeline and thus classified as “UNKNOWN”.

Topic revision: r1 - 2014-11-28 - SusanaBascon
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