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STRADAViT: Self-Supervised Domain Adaptation of Vision Transformer Backbones for Radio Astronomy
Next-generation radio astronomy surveys are delivering millions of resolved sources, yet scalable morphology analysis remains difficult across heterogeneous telescopes and imaging pipelines. We present STRADAViT, a self-supervised continued-pretraining framework for learning transferable radio-astro
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