The main purpose in this wor& is to present some analytical. results about the spatial. distribution of the stars in the Galactic Bulge. These results have been obtained by considering that the stellar system verifies the collisionless Boltzman equation and the ellipsoidal. hypothesis (non-axisymmetrical) for the distribution of peculiar velocities of the stars.
Classification algorithms based on neural network techniques are applied to study if the bulge stars can be differentiated from other stars belonging to other Galactic components. A synthetic sample is build as a mixture of four components, thin disk, thick disk, halo and bulge, according to some stellar system models and considering observational errors.
In this work we study a non-cylindrical point-axial symmetric stellar system model that verifies the Chandrasekhar postulates. It explains the values for the centered moments of the peculiar velocities and the galactic rotation parameters, non possible with a cylindrical model
In this paper we consider a first application of the Learning Vectorial Quantification Neural method (LVQ) to the problem of studying and distinguishing between different populations within an stellar catalogue of the solar neighbourhood (a complete description can be found in Hernández-Pajares and Monte, 1991, Artificial Neural Networks, Ed. A. Prieto, Lecture Notes in Computer Science 540, Springer-Verlag, p.422). It consists, briefly, in the approximation of a set of vectors in a certain characteristic space that contains continuous elements. The representative points for every cluster are the centroids, calculated in such a way to minimize the distortion. Each of those can be labeled with integer numbers using a 2D representation that preserves the neighbouring property in the characteristic space: the Kohonen Map (Kohonen, 1988, IEEE Computer, 21 Nbr.3).