Machine Learning Assisted NEO Discovery and Polarimetric Characterisation with Astronomical Surveys

TL;DR

This paper discusses the development of machine learning algorithms and data analysis platforms to improve the discovery and polarimetric characterization of Near-Earth Objects using astronomical survey data.

Contribution

It introduces novel algorithms and digital platforms specifically designed for machine learning-assisted NEO detection and analysis in surveys not originally intended for this purpose.

Findings

Enhanced detection capabilities for NEOs in survey data

Development of digital platforms for polarimetric analysis

Improved efficiency in NEO discovery processes

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.