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 (150,000 objects), 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 over 10,000 objects, mostly fainted objects and unsuccessful observations, that were rejected by the SDSS spectroscopic classification pipeline and thus classified as “UNKNOWN”. This dataset was transformed to Gaia BP and RP format by the use of GOG simulator.

GUASOM provides a useful toolbox to study the data distribution in extense archives. Even more, the discovered neighbourhood relationships help to unveil the physical nature of objects never observed before. To this effect, we used the SIMBAD catalog to perform crossmatching with the SDSS astrometry, seeking for more identifications.