NA-SODINN: a deep learning algorithm for exoplanet image detection based on residual noise regimes

Supervised deep learning was recently introduced in high-contrast imaging
(HCI) through the SODINN algorithm, a convolutional neural network designed for
exoplanet detection in angular differential imaging (ADI) datasets. The
benchmarking of HCI algorithms within the Exoplanet Imaging Data Challenge
(EIDC) showed that (i) SODINN can produce a high number of false positives in
the final detection maps, and (ii) algorithms processing images in a more local
manner perform better. This work aims to improve the SODINN detection
performance by introducing new local processing approaches and adapting its
learning process accordingly. We propose NA-SODINN, a new deep learning binary
classifier based on a convolutional neural network (CNN) that better captures
image noise correlations in ADI-processed frames by identifying noise regimes.
Our new approach was tested against its predecessor, as well as two
SODINN-based hybrid models and a more standard annular-PCA approach, through
local receiving operating characteristics (ROC) analysis of ADI sequences from
the VLT/SPHERE and Keck/NIRC-2 instruments. Results show that NA-SODINN
enhances SODINN in both sensitivity and specificity, especially in the
speckle-dominated noise regime. NA-SODINN is also benchmarked against the
complete set of submitted detection algorithms in EIDC, in which we show that
its final detection score matches or outperforms the most powerful detection
algorithms.Throughout the supervised machine learning case, this study
illustrates and reinforces the importance of adapting the task of detection to
the local content of processed images.