Machine Learning Assisted NEO Discovery and Polarimetric Characterisation with Astronomical Surveys
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| author | Kleijn, G. A. Verdoes Grobler, T. Chong, S. J. Williams, O. R. Micheli, M. Koschny, D. Saifollahi, T. Koopmans, L. V. E. Dirkx, D. Santana-Ros, T. Ma, Y. -Z. Pöntinen, M. Bagnulo, S. Granvik, M. Irureta-Goyena, B. Y. |
| author_facet | Kleijn, G. A. Verdoes Grobler, T. Chong, S. J. Williams, O. R. Micheli, M. Koschny, D. Saifollahi, T. Koopmans, L. V. E. Dirkx, D. Santana-Ros, T. Ma, Y. -Z. Pöntinen, M. Bagnulo, S. Granvik, M. Irureta-Goyena, B. Y. |
| contents | We are a group of over two dozen astronomers, computer scientists, data scientists and digital Big Data research platform experts at 11 universities and research institutes in South Africa and Europe. We study Near-Earth Objects (NEOs) for Planetary Defence and scientific purposes. We present our research and development programme for algorithms and digital data analysis platforms for machine learning-assisted NEO discovery and polarimetric characterisation in astronomical surveys. Typically, this is serendipitous because these surveys are designed for galactic and extragalactic science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02999 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning Assisted NEO Discovery and Polarimetric Characterisation with Astronomical Surveys Kleijn, G. A. Verdoes Grobler, T. Chong, S. J. Williams, O. R. Micheli, M. Koschny, D. Saifollahi, T. Koopmans, L. V. E. Dirkx, D. Santana-Ros, T. Ma, Y. -Z. Pöntinen, M. Bagnulo, S. Granvik, M. Irureta-Goyena, B. Y. Instrumentation and Methods for Astrophysics We are a group of over two dozen astronomers, computer scientists, data scientists and digital Big Data research platform experts at 11 universities and research institutes in South Africa and Europe. We study Near-Earth Objects (NEOs) for Planetary Defence and scientific purposes. We present our research and development programme for algorithms and digital data analysis platforms for machine learning-assisted NEO discovery and polarimetric characterisation in astronomical surveys. Typically, this is serendipitous because these surveys are designed for galactic and extragalactic science. |
| title | Machine Learning Assisted NEO Discovery and Polarimetric Characterisation with Astronomical Surveys |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2604.02999 |