Speaker
Description
Small near-Earth asteroids (NEAs $<$ 150m) represent the most numerous yet least understood segment of potentially hazardous objects in our Solar System. Their rapid fading after discovery makes it challenging to obtain sufficient follow-up observations for characterisation studies, leaving a critical gap in our knowledge of their taxonomic distribution. We present results from a robotic follow-up program using the South African Astronomical Observatory’s Lesedi telescope. This system uses automated scripts to rapidly identify NEA discoveries reported to the Minor Planet Center (MPC) and execute follow-up observations within hours of detection. Using multi-filter photometry in the g, r, and i bands, we performed taxonomic classification of 59 small NEAs (with absoulute magnitudes H ranging from $22 \leq H < 29$ corresponding to approximates diameters of 97 - 240m and 3.8 to 9.4m respectively) assuming an albedo range of 0.05 to 0.30, representing the typical lower and upper bounds for the most common asteroid taxonomies based on photometric colours, using a trained machine-learning algorithm. Our results reveal that the composition of the small NEA population differs slightly from that of larger NEAs, suggesting size-dependent taxonomic variations relevant to impact hazard assessments. Specifically, we find an approximately 1:1 ratio between stony types (S+V+Q) and carbonaceous/metallic types (C+X), broadly consistent with earlier studies of larger NEAs. However, we identify a significantly higher fraction of X-type asteroids (almost a fourth of the observed sample) compared to previous taxonomic surveys of larger NEAs. This study provides a compositional analysis of sub-150-meter NEAs and suggests that the taxonomic distribution may vary with size, highlighting the importance of dedicated small-object characterisation programs to better understand the most abundant, and thus most likely source of Earth impactors.
| Stream | Science or Engineering |
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