Visual Mining of Astrometric Solution: Let Numerics Meet Art

Space astrometry aims to determine stellar parameters like angular positions and proper motions on the celestial sphere based on satellite telescope observations, which allows for building star catalogues and helps to understand the structure of the Universe. Even small astrometric missions pose Big Data problems and require high-performance computers, as billions of observations must be taken into account to achieve high accuracy in the values of stellar parameters. Astrometric solvers implement complex calibration models to compensate for distortions of observations caused by imperfections in telescope optics and detectors, deviations in satellite attitude, relativistic effects of light bending, etc. We propose leveraging visual data mining to validate the astrometric solution and its underlying calibration model, as well as to profile the performance and numerical stability of astrometric solvers. If the visual means involved are designed well, they possess both cognitive clarity and visual aesthetics. Herewith, visual aesthetics is not only a nice-to-have feature but also an essential part of visual data mining with twofold meaning. First, it makes the research process and results more attractive for scientists, and second, it allows exporting the research to a wider audience, having not only scientific but also artistic and promotional value. In this work, we integrate the ontology-driven semantic visual data mining platform SciVi with the high-performance astrometric solver we made for the Japan Astrometry Satellite Mission for INfrared Exploration (JASMINE). Using the adaptive data processing and rendering capabilities of SciVi, we create an artistic visualisation of the solving process to profile the solver and promote JASMINE.