The direct imaging of exoplanets through 10-m class ground-based telescopes is now a reality of modern astrophysics. Reaching this milestone is the re- sult of significant advances in the field of high-contrast imaging, marked by the development of dedicated telescope instrumentation, including extreme adaptive optics and advanced coronagraphy. However, despite these advance- ments, residual optical aberrations persist, generating quasi-static speckles in the image field of view, whose shape and intensity are similar to potential companions. Over the past two decades, numerous image post-processing techniques have been proposed to further eliminate this residual speckle noise and detect the planet signature. Among these techniques, supervised deep learning was introduced through the SODINN detection algorithm, a binary classifier that uses a convolutional neural network to learn a model that distinguishes between noise and the point-like source in the image. The recent Exoplanet Imaging Data Challenge (EIDC) served as a platform for benchmarking SODINN and other image post-processing techniques. The results revealed that SODINN tends to produce a notable number of false positives, while the most effective techniques rely on mechanisms to capture local image noise dependencies. Building upon these findings, this thesis aims to improve the detection performance of SODINN through capturing new local noise dependencies. Through the development of new statistical methods, we explore the possibility to identify different noise regimes across the angular differential imaging processed image and adapt the SODINN neural network, and its training process, to work under this stratification strategy. This model adaptation leads to the creation of a new detection algo- rithm, called NA-SODINN. Through ROC analyses, NA-SODINN undergoes rigorous testing against its predecessor, demonstrating an improved balance between sensitivity and specificity in detection. Furthermore, NA-SODINN is benchmarked against the full set of detection algorithms submitted in the EIDC. The results indicate that NA-SODINN either matches or exceeds the performance of the most powerful detection algorithms. NA-SODINN is finally used to reanalyze a filtered sample from the recent SHINE exo- planet survey, providing valuable insights and potential exoplanet candidates. 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.

Application of deep learning techniques for exoplanet detection in high contrast imaging