PSF field learning based on Optimal Transport Distances

Understand

Context: in astronomy, observing large fractions of the sky within a reasonable amount of time implies using large field-of-view (fov) optical instruments that typically have a spatially varying Point Spread Function (PSF).

• Depending on the scientific goals, galaxies images need to be corrected for the PSF whereas no direct measurement of the PSF is available.
• Aims: given a set of PSFs observed at random locations, we want to estimate the PSFs at galaxies locations for shapes measurements correction.
• Contributions: we propose an interpolation framework based on Sliced Optimal Transport.