Radio Galaxy Zoo EMU: Harnessing Citizen Science and AI to Advance Open Science Catalogues
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Vardoulaki, Eleni Tang, Hongming Bowles, Micah Segal, Gary Mandhai, Soheb Alexander, Emma L. Williams, Wendy Luo, Yan Rudnick, Lawrence Hopkins, Andrew M. Wong, O. Ivy Shabala, Stanislav S. collaboration, the RGZ EMU |
| author_facet | Vardoulaki, Eleni Tang, Hongming Bowles, Micah Segal, Gary Mandhai, Soheb Alexander, Emma L. Williams, Wendy Luo, Yan Rudnick, Lawrence Hopkins, Andrew M. Wong, O. Ivy Shabala, Stanislav S. collaboration, the RGZ EMU |
| contents | Over the past decades, significant efforts have been devoted to developing sophisticated algorithms for automatically identifying and classifying radio sources in large surveys. However, even the most advanced methods face challenges in recognising complex radio structures and accurately associating radio emission with their host galaxies. Leveraging data from the ASKAP telescope and the Evolutionary Map of the Universe (EMU) survey, Radio Galaxy Zoo EMU (RGZ EMU) was created to generate high-quality radio source classifications for training deep learning models and cataloging millions of radio sources in the southern sky. By integrating novel machine learning techniques, including anomaly detection and natural language processing, our workflow actively engages citizen scientists to enhance classification accuracy. We present results from Phase I of the project and discuss how these data will contribute to improving open science catalogues like EMUCAT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19787 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Radio Galaxy Zoo EMU: Harnessing Citizen Science and AI to Advance Open Science Catalogues Vardoulaki, Eleni Tang, Hongming Bowles, Micah Segal, Gary Mandhai, Soheb Alexander, Emma L. Williams, Wendy Luo, Yan Rudnick, Lawrence Hopkins, Andrew M. Wong, O. Ivy Shabala, Stanislav S. collaboration, the RGZ EMU Astrophysics of Galaxies Instrumentation and Methods for Astrophysics Over the past decades, significant efforts have been devoted to developing sophisticated algorithms for automatically identifying and classifying radio sources in large surveys. However, even the most advanced methods face challenges in recognising complex radio structures and accurately associating radio emission with their host galaxies. Leveraging data from the ASKAP telescope and the Evolutionary Map of the Universe (EMU) survey, Radio Galaxy Zoo EMU (RGZ EMU) was created to generate high-quality radio source classifications for training deep learning models and cataloging millions of radio sources in the southern sky. By integrating novel machine learning techniques, including anomaly detection and natural language processing, our workflow actively engages citizen scientists to enhance classification accuracy. We present results from Phase I of the project and discuss how these data will contribute to improving open science catalogues like EMUCAT. |
| title | Radio Galaxy Zoo EMU: Harnessing Citizen Science and AI to Advance Open Science Catalogues |
| topic | Astrophysics of Galaxies Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2509.19787 |