Isolating the mechanisms for asteroid surface refreshing

Abstract

Evidence is seen for young, fresh surfaces among Near-Earth and Main-Belt asteroids even though space-weathering timescales are shorter than the age of the surfaces. A number of mechanisms have been proposed to refresh asteroid surfaces on short timescales, such as planetary encounters, YORP spinup, thermal degradation, and collisions. Additionally, other factors such as grain size effects have been proposed to explain the existence of these “fresh-looking” spectra. To investigate the role each of these mechanisms may play, we collected a sample of visible and near-infrared spectra of 477 near-Earth and Mars Crosser asteroids with similar sizes and compositions — all with absolute magnitude H > 16 and within the S-complex and having olivine to pyroxene (ol/(ol+opx)) ratios >0.65. We taxonomically classify these objects in the Q (fresh) and S (weathered) classes. We find four trends in the Q/S ratio: (1) previous work demonstrated the Q/S ratio increases at smaller sizes down to H ≲16, but we find a sharp increase near H∼19 after which the ratio decreases monotonically. (2) in agreement with many previous studies, the Q/S ratio increases with decreasing perihelion distance, and we find it is non-zero for larger perihelia >1.2AU, (3) as a new finding our work reveals the Q/S ratio has a sharp, significant peak near ∼5° orbital inclination, and (4) we confirm previous findings that the Q/S ratio is higher for objects that have the possibility of encounter with Earth and Venus versus those that do not, however this finding cannot be distinguished from the perihelion trend. No single resurfacing mechanism can explain all of these trends, so multiple mechanisms are required. YORP spin-up scales with size, thermal degradation is dependent on perihelion, planetary encounters trend with inclination, perihelion and MOID, noting that asteroid–asteroid collisions are also dependent on inclination. It is likely that a combination of all four resurfacing mechanisms are needed to account for all observational trends.