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Most of the high-contrast imaging (HCI) data-processing techniques used overthe 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 calledregime switching model (RSM) map has been proposed to take advantage of thesenumerous PSF-subtraction techniques; RSM uses several of these techniques togenerate a single probability map. Selection of the optimal parameters forthese PSF-subtraction techniques as well as for the RSM map is notstraightforward, is time consuming, and can be biased by assumptions made as tothe underlying data set. We propose a novel optimisation procedure that can beapplied to each of the PSF-subtraction techniques alone, or to the entire RSMframework. The optimisation procedure consists of three main steps: (i)definition of the optimal set of parameters for the PSF-subtraction techniquesusing the contrast as performance metric, (ii) optimisation of the RSMalgorithm, and (iii) selection of the optimal set of PSF-subtraction techniquesand ADI sequences used to generate the final RSM probability map. Theoptimisation procedure is applied to the data sets of the exoplanet imagingdata challenge (EIDC), which provides tools to compare the performance of HCIdata-processing techniques. The data sets consist of ADI sequences obtainedwith three state-of-the-art HCI instruments: SPHERE, NIRC2, and LMIRCam. Theresults of our analysis demonstrate the interest of the proposed optimisationprocedure, with better performance metrics compared to the earlier version ofRSM, as well as to other HCI data-processing techniques.

Auto-RSM: an automated parameter-selection algorithm for the RSM map exoplanet detection algorithm