Taxonomic Distribution of the Small Near-Earth Asteroid Population

Abstract Small near-Earth asteroids (NEAs), diameters < 150 m, represent the most numerous yet one of the least well-understood populations among near-Earth objects, despite their potential hazard. 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 and execute follow-up observations within hours of detection. Using multifilter photometry in the g , r , and i bands, we performed taxonomic classification of 59 small NEAs, with absolute magnitudes H ranging from 22 ≤ H < 29, using a trained machine learning algorithm. Our results reveal that the composition of the small NEA population slightly differs from the population of larger size, pointing to 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 third of the observed sample) compared to previous taxonomic surveys of larger NEAs. This study provides a compositional analysis of sub-150 m NEAs and suggests that the taxonomic distribution may vary with size, highlighting the importance of dedicated small-object characterization programs to better understand the most abundant, and thus most likely source of Earth impactors.