  Most of the high-contrast imaging (HCI) data-processing techniques used over
the last 15 years have relied on the angular differential imaging (ADI)
observing strategy, along with subtraction of a reference point spread function
(PSF) to generate exoplanet detection maps. Recently, a new algorithm called
regime switching model (RSM) map has been proposed to take advantage of these
numerous PSF-subtraction techniques; RSM uses several of these techniques to
generate a single probability map. Selection of the optimal parameters for
these PSF-subtraction techniques as well as for the RSM map is not
straightforward, is time consuming, and can be biased by assumptions made as to
the underlying data set. We propose a novel optimisation procedure that can be
applied to each of the PSF-subtraction techniques alone, or to the entire RSM
framework. The optimisation procedure consists of three main steps: (i)
definition of the optimal set of parameters for the PSF-subtraction techniques
using the contrast as performance metric, (ii) optimisation of the RSM
algorithm, and (iii) selection of the optimal set of PSF-subtraction techniques
and ADI sequences used to generate the final RSM probability map. The
optimisation procedure is applied to the data sets of the exoplanet imaging
data challenge (EIDC), which provides tools to compare the performance of HCI
data-processing techniques. The data sets consist of ADI sequences obtained
with three state-of-the-art HCI instruments: SPHERE, NIRC2, and LMIRCam. The
results of our analysis demonstrate the interest of the proposed optimisation
procedure, with better performance metrics compared to the earlier version of
RSM, as well as to other HCI data-processing techniques.
