Self-organizing maps
Component-based visual clustering using the self-organizing map
Neural Networks
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We present the results of an unsupervised classification of the disk and halo white dwarf populations in the solar neighborhood. The classification is done by merging the results of detailed Monte Carlo (MC) simulations, which reproduce very well the characteristics of the white dwarf populations in the solar neighborhood, with a catalogue of real stars. The resulting composite catalogue is analyzed using a competitive learning algorithm. In particular we have used the so-called self-organized map. The MC simulated stars are used as tracers and help in identifying the resulting clusters. The results of such an strategy turn out to be quite satisfactory, suggesting that this approach can provide an useful framework for analyzing large databases of white dwarfs with well determined kinematical, spatial and photometric properties once they become available in the next decade. Moreover, the results are of astrophysical interest as well, since a straightforward interpretation of several recent astronomical observations, like the detected microlensing events in the direction of the Magellanic Clouds, the possible detection of high proper motion white dwarfs in the Hubble Deep Field and the discovery of high velocity white dwarfs in the solar neighborhood, suggests that a fraction of the baryonic dark matter component of our galaxy could be in the form of old and dim halo white dwarfs.