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.

翻译：系外行星的直接成像探测是一项艰巨任务：目标天体的微弱信号被主星引起的空间结构化噪声成分所掩盖。只有结合多次观测数据并运用专用探测算法，才能识别系外行星信号。与现有大多数方法不同，我们提出直接从观测数据中学习噪声的空间、时间及光谱特征模型。在预处理阶段，我们局部建立噪声相关性的统计模型，并对数据进行中心化和白化处理，以改善其平稳性和信噪比。随后采用卷积神经网络进行有监督训练，检测预处理图像中合成源的残余信号。该方法在精度与召回率之间实现了优于领域内标准方法的平衡，且超越了仅基于统计框架的现有最优算法。此外，相较于仅利用时空数据的同类模型，对光谱多样性的利用进一步提升了探测性能。