Developing instrumentation and software for rapid follow-up and characterisation of near-Earth Asteroids

Ngwane, Thobekile sandra

Developing instrumentation and software for rapid follow-up and characterisation of near-Earth Asteroids

Thesis / Dissertation

2025

Publisher

University of Cape Town

Department

Department of Astronomy

Faculty

Faculty of Science

Abstract

Near-Earth Asteroids (NEAs), a subset of minor bodies in the Solar System, result from resonant interactions with major planets, particularly Jupiter, leading to their escape from the main asteroid belt. The International Astronomical Union's Minor Planet Center (MPC) database, as of December 2024, lists approximately 37,000 discovered NEAs, with an average daily discovery rate of 10 from dedicated survey programs like Catalina Sky Survey (CSS), the Panoramic Survey Telescope and Rapid Response System (PanSTARRS), and the Asteroid Terrestrial-impact Last Alert System (ATLAS). This project uses the robotic observing capabilities of the South African Astronomical Observatory's 1-meter telescope, Lesedi, equipped with the Mookodi instrument. Observations are scheduled in robotic mode using automated Python scripts, enabling rapid follow-up of newly discovered NEAs, often within the same night of detection. This rapid response is essential, as smaller asteroids (< 300 metres)—a significantly understudied group— quickly dim as they move away from Earth, making precise measurements challenging. Since the start of this project in February 2023, approximately 230 NEAs have been successfully observed in robotic mode, with an average absolute magnitude (H-magnitude) of 24.4. This magnitude corresponds to asteroid sizes ranging from 32 to 78 metres, depending on an assumed albedo of 0.05 to 0.30. Approximately 75% of these asteroids have a diameter (D) of less than 100 metres. Among the observed NEAs, 15 have been classified as potentially hazardous asteroids (PHAs). The findings presented in this study are based on multi-filter photometry and astrometric measurements collected as part of the program. The astrometric data significantly contributes to the MPC's orbital refinement and the observed NEAs designation. Photometric observations using g, r, and i filters enable the extraction of g - r and r - i colours, which approximate the spectral slope. These colours aid in determining the most likely taxonomic type (S, C, X, D, Q, or V-types in this project) of the observed NEAs, as defined by the Bus-DeMeo Classification Scheme. This provides insight into their composition. Using the collected data, the compositional distribution of the small NEA population was determined and compared with previous studies investigating a larger size population.

Keywords

Catalina Sky Survey

Earth Asteroids

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