Machine Learning Assisted NEO Discovery and Polarimetric Characterisation with Astronomical Surveys
Instrumentation and Methods for Astrophysics
2026-04-06 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2604.02999,
title = {Machine Learning Assisted NEO Discovery and Polarimetric Characterisation with Astronomical Surveys},
author = {G. A. Verdoes Kleijn and T. Grobler and S. J. Chong and O. R. Williams and M. Micheli and D. Koschny and T. Saifollahi and L. V. E. Koopmans and D. Dirkx and T. Santana-Ros and Y. -Z. Ma and M. Pöntinen and S. Bagnulo and M. Granvik and B. Y. Irureta-Goyena},
journal= {arXiv preprint arXiv:2604.02999},
year = {2026}
}
Comments
5 pages, extended abstract for the 9th IAA Planetary Defense Conference 5-9 May 2025, Stellenbosch, Cape Town, South Africa