Source finding is one of the most challenging tasks in upcoming radio continuum surveys with SKA precursors, such as the Evolutionary Map of the Universe (EMU) survey of the Australian SKA Pathfinder (ASKAP) telescope. The resolution, sensitivity, and sky coverage of such surveys is unprecedented, requiring new features and improvements to be made in existing source finders. Among them, reducing the false detection rate, particularly in the Galactic plane, and the ability to associate multiple disjoint islands into physical objects. To bridge this gap, we developed a new source finder, based on the Mask R-CNN object detection framework, capable of both detecting and classifying compact, extended, spurious, and poorly imaged sources in radio continuum images. The model was trained using ASKAP EMU data, observed during the Early Science and pilot survey phase, and previous radio survey data, taken with the VLA and ATCA telescopes. On the test sample, the final model achieves an overall detection completeness above 85%, a reliability of ∼65%, and a classification precision/recall above 90%. Results obtained for all source classes are reported and discussed.

Riggi, S., Magro, D., Sortino, R., De Marco, A., Bordiu, C., Cecconello, T., et al. (2023). Astronomical source detection in radio continuum maps with deep neural networks. ASTRONOMY AND COMPUTING, 42(January 2023) [10.1016/j.ascom.2022.100682].

Astronomical source detection in radio continuum maps with deep neural networks

Cecconello, T.;
2023

Abstract

Source finding is one of the most challenging tasks in upcoming radio continuum surveys with SKA precursors, such as the Evolutionary Map of the Universe (EMU) survey of the Australian SKA Pathfinder (ASKAP) telescope. The resolution, sensitivity, and sky coverage of such surveys is unprecedented, requiring new features and improvements to be made in existing source finders. Among them, reducing the false detection rate, particularly in the Galactic plane, and the ability to associate multiple disjoint islands into physical objects. To bridge this gap, we developed a new source finder, based on the Mask R-CNN object detection framework, capable of both detecting and classifying compact, extended, spurious, and poorly imaged sources in radio continuum images. The model was trained using ASKAP EMU data, observed during the Early Science and pilot survey phase, and previous radio survey data, taken with the VLA and ATCA telescopes. On the test sample, the final model achieves an overall detection completeness above 85%, a reliability of ∼65%, and a classification precision/recall above 90%. Results obtained for all source classes are reported and discussed.
Articolo in rivista - Articolo scientifico
Deep learning; Instance segmentation; Neural networks; Radio continuum; SKA precursors; Source finding;
English
8-dic-2022
2023
42
January 2023
100682
none
Riggi, S., Magro, D., Sortino, R., De Marco, A., Bordiu, C., Cecconello, T., et al. (2023). Astronomical source detection in radio continuum maps with deep neural networks. ASTRONOMY AND COMPUTING, 42(January 2023) [10.1016/j.ascom.2022.100682].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/400795
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