Point Proposal Network: Accelerating Point Source Detection Through Deep Learning
This addresses the problem of efficient point source detection for astronomers, especially with upcoming large-scale surveys like the SKA, but it is incremental as it builds on existing deep learning techniques.
The paper tackles the challenge of detecting point sources in large radio astronomical images by proposing the Point Proposal Network (PPN), a deep learning-based detector that achieves faster detection and scalability to terapixel images, though with less precision than leading methods.
Point source detection techniques are used to identify and localise point sources in radio astronomical surveys. With the development of the Square Kilometre Array (SKA) telescope, survey images will see a massive increase in size from Gigapixels to Terapixels. Point source detection has already proven to be a challenge in recent surveys performed by SKA pathfinder telescopes. This paper proposes the Point Proposal Network (PPN): a point source detector that utilises deep convolutional neural networks for fast source detection. Results measured on simulated MeerKAT images show that, although less precise when compared to leading alternative approaches, PPN performs source detection faster and is able to scale to large images, unlike the alternative approaches.