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Deep Radio Image Segmentation

Published online by Cambridge University Press:  01 August 2025

Hattie Stewart Open the ORCID record for Hattie Stewart [Opens in a new window] ,
Mark Birkinshaw  and
Jason Yeung
Hattie Stewart*
Affiliation:
University of Bristol Rutherford Appleton Laboratory
Mark Birkinshaw
Affiliation:
University of Bristol
Jason Yeung
Affiliation:
Rutherford Appleton Laboratory
*
email: hattie.stewart@bristol.ac.uk
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Abstract

We show that the U-Net neural network architecture provides an efficient and effective way of locating sources in SKA Data Challenge datasets. The improved performance relative to PyBDSF is quantified and U-Net is proposed as an efficient source finder for real radio surveys.

Keywords

Radio SurveysMachine LearningSquare Kilometre Array

Information

Type
Poster Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 127 - 128
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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References

Bonaldi, A., An, T., Brüggen, M., et al. 2021, MNRAS, 500, 3821 Google Scholar
Mohan, N. & Rafferty, D. 2015, Python Blob Detection and Source Finder, Astrophysics Source Code Library, ascl:1502.007Google Scholar
Ronneberger, O., Fischer, P. & Brox, T. 2015 in: Navab, N. et al. (eds.), Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science 9351, p. 234 (Springer)Google Scholar