Analysis of convolutional neural networks and vision transformers in direct imaging for high-contrast exoplanet detection

Direct imaging of exoplanets involves collecting photons emitted or reflected by a planet orbiting a host star. This approach is challenging due to the extreme brightness difference between exoplanets and their host stars as well as the small angular separation between a star and its orbiting exoplanet. Coronagraphs and deformable mirrors are used to block and redistribute the starlight to enable imaging of the exoplanets. Speckle discrimination algorithms aim to find potential exoplanets but struggle to distinguish actual planetary signals from residual starlight speckles, which can lead to false positives. Existing speckle discrimination methods rely on binary classification rather than probabilistic outputs, limiting their ability to differentiate between exoplanets and noise based on subtle patterns. These algorithms operate in the post-processing stage and do not autonomously follow up on detected points of interest by refining observational parameters, such as spectral bands, exposure time, or region of interest. In this work, we apply deep learning models, specifically Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), to high-contrast exoplanet detection. CNNs are effective at detecting local spatial features, making them well-suited for identifying small, well-defined planetary signals. ViTs leverage self-attention mechanisms to capture long-range dependencies, which may improve their ability to distinguish exoplanets from complex noise patterns. The models are trained and tested using images from the High-contrast imager for Complex Aperture Telescopes (HiCAT) simulator developed by the Space Telescope Science Institute, with synthetic exoplanets injected into raw testbed images. By comparing the performance of CNNs and ViTs, we assess their suitability for future exoplanet detection efforts. This study highlights how AI-driven approaches can address the growing demands of next-generation observatories by enhancing detection sensitivity, reducing false positives, and enabling real-time follow-up actions to refine imaging parameters.