Grammar evolution and symbolic regression for astrometric centering of Hubble Space Telescope images

Symbolic regression, in general, and genetic models, in particular, are promising approaches to mathematical modeling in astrometry where it is not always clear which is the fittest analytic expression depending on the problem under consideration. Several attempts and increasing research efforts are being made in this direction mainly from the Genetic Programming (GP) viewpoint. Our proposal is, as far as we know, the first one to apply Grammatical Evolution (GE) in this domain. GE (and further GE extensions) aim to outperform GP limitations by incorporating formal languages tools to guarantee the correctness (both syntactic and semantic) of the generated expressions. The current contribution is a first proof to check the viability of GE on astrometric real datasets. Its success in finding adequate parameters for predefined families of functions in star centering (Gaussian and Moffat PSFs) with simple and naive GE experiments supports our hypothesis on taking advantage of the expressive power of GE to tackle astrometry scenarios of interest and hence greatly improve current astrometric software thanks to specific genetic approaches.