Automated learning of gravitational mass of elliptical galaxies

We present a 3-staged method for automated learning of the spatial density function of the mass of all gravitating matter in a real galaxy, for which, data exist on the observable phase space coordinates of a sample of resident galactic particles that trace the galactic gravitational potential. We learn this gravitational mass density function, by embedding it in the domain of the probability density function (pdf) of the phase space vector variable, where we learn this pdfas well, given the data. We generate values of each sought function, at a design value of its input, to learn vectorised versions of each function; this creates the training data, using which we undertake supervised learning of each function, to thereafter undertake predictions and forecasting of the functional value, at test inputs. We assume that the phase space that a kinematic data set is sampled from, is isotropic, and we quantify the relative violation of this assumption, in a given data set. Illustration of the method is made to the real elliptical galaxy NGC4649. The purpose of this learning is to produce a data-driven protocol that allows for computation of dark matter content in any example real galaxy, without relying on system- specific astronomical details, while undertaking objective quantification of support in the data for undertaken model assumptions.