Combining multi-spectral data with statistical and deep-learning models
  for improved exoplanet detection in direct imaging at high contrast
Olivier Flasseur Théo Bodrito Julien Mairal Jean Ponce Maud Langlois Anne-Marie Lagrange
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 combining 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.