  We present an innovative approach to the methodology of dynamical modelling,
allowing practical reconstruction of the underlying dark matter mass without
assuming both the density and anisotropy functions. With this, the
mass-anisotropy degeneracy is reduced to simple model inference, incorporating
the uncertainties inherent with observational data, statistically circumventing
the mass-anisotropy degeneracy in spherical collisionless systems. We also
tackle the inadequacy that the Jeans method of moments has on small datasets,
with the aid of Generative Adversarial Networks: we leverage the power of
artificial intelligence to reconstruct non-parametrically the projected
line-of-sight velocity distribution. We show with realistic numerical
simulations of dwarf spheroidal galaxies that we can distinguish between
competing dark matter distributions and recover the anisotropy and mass profile
of the system.
