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| Paper: |
Detection and Classification of Radio Sources with Deep Learning |
| Volume: |
541, ADASS XXXIII |
| Page: |
177 |
| Authors: |
Simone Riggi; Thomas Cecconello; Ugo Becciani; F. Vitello |
| DOI: |
10.26624/JTVC6453 |
| 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. |
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