RSM detection map for direct exoplanet detection in ADI sequences

TL;DR

This paper introduces a novel regime-switching detection map for ADI sequences in exoplanet imaging, improving detection performance by leveraging a model inspired by econometrics to distinguish planetary signals from residual noise.

Contribution

It proposes a new detection algorithm based on regime-switching models that integrates residual cubes from various ADI techniques into a single detection map, enhancing detection accuracy.

Findings

Better ROC performance compared to standard SNR maps

Effective on data from VLT/NACO and VLT/SPHERE

Versatile use of residual cubes from different ADI methods

Abstract

Beyond the choice of wavefront control systems or coronographs, advanced data processing methods play a crucial role in disentangling potential planetary signals from bright quasi-static speckles. Among these methods, angular differential imaging (ADI) for data sets obtained in pupil tracking mode (ADI sequences) is one of the foremost research avenues, considering the many observing programs performed with ADI-based techniques and the associated discoveries. Inspired by the field of econometrics, here we propose a new detection algorithm for ADI sequences, deriving from the regime-switching model first proposed in the 1980s. The proposed model is very versatile as it allows the use of PSF-subtracted data sets (residual cubes) provided by various ADI-based techniques, separately or together, to provide a single detection map. The temporal structure of the residual cubes is used for the detection as the model is fed with a concatenated series of pixel-wise time sequences. The algorithm provides a detection probability map by considering two possible regimes for concentric annuli, the first one accounting for the residual noise and the second one for the planetary signal in addition to the residual noise. The algorithm performance is tested on data sets from two instruments, VLT/NACO and VLT/SPHERE. The results show an overall better performance in the receiver operating characteristic space when compared with standard signal-to-noise-ratio maps for several state-of-the-art ADI-based post-processing algorithms.