Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

Abstract

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.

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Cited by in corpus (4)

• YOLO-CIANNA: Galaxy detection with deep learning in radio data. I. A new YOLO-inspired source detection method applied to the SKAO SDC1
• Sifting the debris: Patterns in the SNR population with unsupervised ML methods
• Transformer-Based Source Detection and Morphological Classification in LOFAR Deep-Field Continuum Images
• Galaxy clusters in the LoTSS-DR3: Catalogues and detection pipeline for diffuse radio emission