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High-contrast imaging (HCI) is one of the most challenging techniques forexoplanet detection. It relies on sophisticated data processing to reach highcontrasts at small angular separations. Most data processing techniques of thistype are based on the angular differential imaging (ADI) observing strategy toperform the reference PSF subtraction, and generally make use ofsignal-to-noise (S/N) maps to infer the existence of planetary signals viathresholding. An alternative method for generating the final detection map wasrecently proposed with the regime-switching model (RSM) map, which uses aregime-switching framework to generate a probability map based on cubes ofresiduals generated by different PSF subtraction techniques. In this paper, wepresent several improvements to the original RSM map, focusing on novel PSFsubtraction techniques and their optimal combinations, as well as a newprocedure for estimating the probabilities involved. We started by implementingtwo forward-model versions of the RSM map algorithm based on the LOCI and KLIPPSF subtraction techniques. We then addressed the question of optimallyselecting the PSF subtraction techniques to optimise the overall performance ofthe RSM map. A new forward-backward approach was also implemented to take intoaccount both past and future observations to compute the RSM map probabilities,leading to improved precision in terms of astrometry and lowering thebackground speckle noise. We tested the ability of these various improvementsto increase the performance of the RSM map based on different data sets via acomputation of ROC curves. These results demonstrate the benefits of theseproposed improvements. Finally, we present a new framework to generate contrastcurves based on probability maps. The contrast curves highlight the higherperformance of the RSM map compared to a standard S/N map at small angularseparations.

Improving the RSM map exoplanet detection algorithm: PSF forward modelling and optimal selection of PSF subtraction techniques