Detection and Classification of Radio Sources with Deep Learning
Abstract
In this paper we present three different applications, based on deep learning methodologies, that we are developing to support the scientific analysis conducted within the ASKAP-EMU and MeerKAT radio surveys. One employs instance segmentation frameworks to detect compact and extended radio sources and imaging artifacts from radio continuum images. Another application uses gradient boosting decision trees and convolutional neural networks to classify compact sources into different astronomical classes using combined radio and infrared multi-band images. Finally, we discuss how self-supervised learning can be used to obtain valuable radio data representations for source detection, and classification studies.
Fields of Study & Subjects
Speech Recognition and Synthesis
Geophysical Methods and Applications
Terahertz technology and applications
Publication details
- Journal / source
- Astronomical Society of the Pacific Conference Series
- Publisher
- Astronomical Society of the Pacific Conference Series
- Volume
- 541
- Pages
- 177–180
- Publication date
- 28 February 2026
- Language
- EN