Scheme of our CNN architecture. The CNN extracts the spatial and dynamical information of the galaxy from the projected stellar data and compresses it through a series of convolution and pooling operations. The CNN joins the information of the spatial and dynamical branches and further reduces the dimensionality for producing an N parameter output, representing the value estimated for the dynamical mass of the galaxy enclosed within N different radii. After training the CNN, the 32 neurons of penultimate layer are used as inputs to train a normalising flow model. The flow model learns a series of transformations to a N-dimensional Gaussian PDF, which are conditioned on the inputs, and outputs a posterior N-dimensional joint PDF for the N enclosed masses.