Radio astronomical images object detection and segmentation: A benchmark on deep learning methods
This provides a practical guide for astrophysicists to apply machine learning for source finding in the Big Data era of radio astronomy, but it is incremental as it evaluates existing methods on new data.
The paper benchmarks deep learning methods for object detection and segmentation in radio astronomical images, finding that existing models can achieve high performance but vary in computational efficiency.
In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest telescope in the world - the Square Kilometre Array (SKA), the task of automatic object detection and instance segmentation is crucial for source finding and analysis. In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical images obtained by radio interferometric instrumentation, to solve the task of automatic source detection. This is carried out by applying models designed to accomplish two different kinds of tasks: object detection and semantic segmentation. The goal is to provide an overview of existing techniques, in terms of prediction performance and computational efficiency, to scientists in the astrophysics community who would like to employ machine learning in their research.