Visibility Data

Visibility data, primarily from radio astronomy but applicable to other imaging modalities, represents the raw signal before image reconstruction, often containing noise and artifacts. Current research focuses on improving the quality of this data through techniques like semi-supervised learning, leveraging both labeled and unlabeled data to train models for denoising and anomaly detection (e.g., radio frequency interference). These advancements, employing methods such as denoising diffusion probabilistic models and singular value decomposition-based compression, aim to enhance image reconstruction, leading to improved sensitivity and resolution in astronomical observations and other applications requiring robust image processing from noisy data. The resulting improvements in data quality have significant implications for scientific discovery and technological advancements in various fields.