Point Proposal Network: Accelerating Point Source Detection Through Deep Learning

TL;DR

This paper introduces the Point Proposal Network, a deep learning-based method for rapid point source detection in large radio astronomical images, demonstrating faster performance and scalability despite slightly lower accuracy.

Contribution

The paper presents a novel deep convolutional neural network architecture specifically designed for fast and scalable point source detection in large-scale radio survey images.

Findings

PPN detects sources faster than existing methods.

PPN scales efficiently to large images.

PPN has slightly lower precision compared to leading approaches.

Abstract

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.