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”.