Combining Multi-Spectral Data With Statistical and Deep-Learning Models for Improved Exoplanet Detection in Direct Imaging at High Contrast

• Published in: 2023 31st European Signal Processing Conference (EUSIPCO) pp. 1723 - 1727
• Main Authors: Flasseur, Olivier, Bodrito, Theo, Mairal, Julien, Ponce, Jean, Langlois, Maud, Lagrangev, Anne-Marie
• Format: Conference Proceeding
• Language: English
• Published: EURASIP 04.09.2023
• Subjects: correlated data; detection; matched filter; multi-variate data; supervised deep learning
• ISSN: 2076-1465
• DOI: 10.23919/EUSIPCO58844.2023.10289870

Exoplanet detection by direct imaging is a difficult task: the faint signals from the objects of interest are buried under a spatially structured nuisance component induced by the host star. The exoplanet signals can only be identified when com-bining several observations with dedicated detection algorithms. In contrast to most of existing methods, we propose to learn a model of the spatial, temporal and spectral characteristics of the nuisance, directly from the observations. In a pre-processing step, a statistical model of their correlations is built locally, and the data are centered and whitened to improve both their stationarity and signal-to-noise ratio (SNR). A convolutional neural network (CNN) is then trained in a supervised fashion to detect the residual signature of synthetic sources in the pre-processed images. Our method leads to a better trade-off between precision and recall than standard approaches in the field. It also outperforms a state-of-the-art algorithm based solely on a statistical framework. Besides, the exploitation of the spectral diversity improves the performance compared to a similar model built solely from spatio-temporal data.