Neural network correction of astrometric chromaticity

In this paper, we deal with the problem of chromaticity, i.e. apparent position variation of stellar images with their spectral distribution, using neural networks (NNs) to analyse and process astronomical images. The goal is to remove this relevant source of systematic error in the data reduction of high-precision astrometric experiments, like Gaia. This task can be accomplished thanks to the capability of NNs to solve a non-linear approximation problem, i.e. to construct a hypersurface that approximates a given set of scattered data couples. Images are encoded associating each of them with conveniently chosen moments, evaluated along the y-axis. The technique proposed, in the current framework, reduces the initial chromaticity of a few milliarcseconds to values of few microarcseconds.