  High-contrast imaging (HCI) is one of the most challenging techniques for
exoplanet detection. It relies on sophisticated data processing to reach high
contrasts at small angular separations. Most data processing techniques of this
type are based on the angular differential imaging (ADI) observing strategy to
perform the reference PSF subtraction, and generally make use of
signal-to-noise (S/N) maps to infer the existence of planetary signals via
thresholding. An alternative method for generating the final detection map was
recently proposed with the regime-switching model (RSM) map, which uses a
regime-switching framework to generate a probability map based on cubes of
residuals generated by different PSF subtraction techniques. In this paper, we
present several improvements to the original RSM map, focusing on novel PSF
subtraction techniques and their optimal combinations, as well as a new
procedure for estimating the probabilities involved. We started by implementing
two forward-model versions of the RSM map algorithm based on the LOCI and KLIP
PSF subtraction techniques. We then addressed the question of optimally
selecting the PSF subtraction techniques to optimise the overall performance of
the RSM map. A new forward-backward approach was also implemented to take into
account both past and future observations to compute the RSM map probabilities,
leading to improved precision in terms of astrometry and lowering the
background speckle noise. We tested the ability of these various improvements
to increase the performance of the RSM map based on different data sets via a
computation of ROC curves. These results demonstrate the benefits of these
proposed improvements. Finally, we present a new framework to generate contrast
curves based on probability maps. The contrast curves highlight the higher
performance of the RSM map compared to a standard S/N map at small angular
separations.
