Radio Galaxy Zoo: CLARAN - A deep learning classifier for radio morphologies
The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification through visual inspection unfeasible.We present CLARAN-Classifying Radio sources Automatically with Neural networks - a p...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Journal Article |
| Published: |
Oxford University Press
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/73873 |
| _version_ | 1848763121097768960 |
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| author | Wu, C. Wong, O. Rudnick, L. Shabala, S. Alger, M. Banfield, J. Ong, C. White, Sarah Garon, A. Norris, R. Andernach, H. Tate, J. Lukic, V. Tang, H. Schawinski, K. Diakogiannis, F. |
| author_facet | Wu, C. Wong, O. Rudnick, L. Shabala, S. Alger, M. Banfield, J. Ong, C. White, Sarah Garon, A. Norris, R. Andernach, H. Tate, J. Lukic, V. Tang, H. Schawinski, K. Diakogiannis, F. |
| author_sort | Wu, C. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification through visual inspection unfeasible.We present CLARAN-Classifying Radio sources Automatically with Neural networks - a proof-of-concept radio source morphology classifier based upon the Faster Region-based Convolutional Neutral Networks method. Specifically, we train and test CLARAN on the FIRST and WISE (Wide-field Infrared Survey Explorer) images from the Radio Galaxy Zoo Data Release 1 catalogue. CLARAN provides end users with automated identification of radio source morphology classifications from a simple input of a radio image and a counterpart infrared image of the same region. CLARAN is the first open-source, endto- end radio source morphology classifier that is capable of locating and associating discrete and extended components of radio sources in a fast (<200 ms per image) and accurate (=90 per cent) fashion. Future work will improve CLARAN's relatively lower success rates in dealing with multisource fields and will enable CLARAN to identify sources on much larger fields without loss in classification accuracy. |
| first_indexed | 2025-11-14T10:58:25Z |
| format | Journal Article |
| id | curtin-20.500.11937-73873 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:58:25Z |
| publishDate | 2019 |
| publisher | Oxford University Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-738732019-03-13T03:26:36Z Radio Galaxy Zoo: CLARAN - A deep learning classifier for radio morphologies Wu, C. Wong, O. Rudnick, L. Shabala, S. Alger, M. Banfield, J. Ong, C. White, Sarah Garon, A. Norris, R. Andernach, H. Tate, J. Lukic, V. Tang, H. Schawinski, K. Diakogiannis, F. The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification through visual inspection unfeasible.We present CLARAN-Classifying Radio sources Automatically with Neural networks - a proof-of-concept radio source morphology classifier based upon the Faster Region-based Convolutional Neutral Networks method. Specifically, we train and test CLARAN on the FIRST and WISE (Wide-field Infrared Survey Explorer) images from the Radio Galaxy Zoo Data Release 1 catalogue. CLARAN provides end users with automated identification of radio source morphology classifications from a simple input of a radio image and a counterpart infrared image of the same region. CLARAN is the first open-source, endto- end radio source morphology classifier that is capable of locating and associating discrete and extended components of radio sources in a fast (<200 ms per image) and accurate (=90 per cent) fashion. Future work will improve CLARAN's relatively lower success rates in dealing with multisource fields and will enable CLARAN to identify sources on much larger fields without loss in classification accuracy. 2019 Journal Article http://hdl.handle.net/20.500.11937/73873 10.1093/mnras/sty2646 Oxford University Press fulltext |
| spellingShingle | Wu, C. Wong, O. Rudnick, L. Shabala, S. Alger, M. Banfield, J. Ong, C. White, Sarah Garon, A. Norris, R. Andernach, H. Tate, J. Lukic, V. Tang, H. Schawinski, K. Diakogiannis, F. Radio Galaxy Zoo: CLARAN - A deep learning classifier for radio morphologies |
| title | Radio Galaxy Zoo: CLARAN - A deep learning classifier for radio morphologies |
| title_full | Radio Galaxy Zoo: CLARAN - A deep learning classifier for radio morphologies |
| title_fullStr | Radio Galaxy Zoo: CLARAN - A deep learning classifier for radio morphologies |
| title_full_unstemmed | Radio Galaxy Zoo: CLARAN - A deep learning classifier for radio morphologies |
| title_short | Radio Galaxy Zoo: CLARAN - A deep learning classifier for radio morphologies |
| title_sort | radio galaxy zoo: claran - a deep learning classifier for radio morphologies |
| url | http://hdl.handle.net/20.500.11937/73873 |