Strong gravitational lensing is a powerful tool for studying dark matter and the large-scale structure of the universe. This project focuses on developing a vision foundation model specifically designed for lensing data, which can be fine-tuned for a variety of downstream tasks, including classification, super-resolution, regression, and lens finding.
This project will explore different training strategies such as self-supervised learning, contrastive learning, or transformer-based models to learn meaningful representations of lensing images. By leveraging diverse datasets and training methodologies, the model will serve as a general-purpose backbone that can adapt to different astrophysical tasks while improving generalization across various observational conditions.
Total project length: 350 hours.
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