  The detection of exoplanets in high-contrast imaging (HCI) data hinges on
post-processing methods to remove spurious light from the host star. So far,
existing methods for this task hardly utilize any of the available domain
knowledge about the problem explicitly. We propose a new approach to HCI
post-processing based on a modified half-sibling regression scheme, and show
how we use this framework to combine machine learning with existing scientific
domain knowledge. On three real data sets, we demonstrate that the resulting
system performs clearly better (both visually and in terms of the SNR) than one
of the currently leading algorithms. If further studies can confirm these
results, our method could have the potential to allow significant discoveries
of exoplanets both in new and archival data.
