A machine-generated catalogue of Charon's craters and implications for the Kuiper belt
This provides an independent confirmation of prior findings about Kuiper Belt object populations, addressing a debate in planetary science, but is incremental as it applies an existing method to new data with slight refinements.
The paper tackles the problem of verifying the size distribution of Charon's craters to infer properties of the Kuiper Belt, using a deep learning model that independently confirms a change in slope around 15 km with best-fit slopes of q = -1.47 ± 0.33 for craters smaller than 10 km and q = -2.91 ± 0.51 for craters larger than 15 km.
In this paper we investigate Charon's craters size distribution using a deep learning model. This is motivated by the recent results of Singer et al. (2019) who, using manual cataloging, found a change in the size distribution slope of craters smaller than 12 km in diameter, translating into a paucity of small Kuiper Belt objects. These results were corroborated by Robbins and Singer (2021), but opposed by Morbidelli et al. (2021), necessitating an independent review. Our MaskRCNN-based ensemble of models was trained on Lunar, Mercurian, and Martian crater catalogues and both optical and digital elevation images. We use a robust image augmentation scheme to force the model to generalize and transfer-learn into icy objects. With no prior bias or exposure to Charon, our model find best fit slopes of q =-1.47+-0.33 for craters smaller than 10 km, and q =-2.91+-0.51 for craters larger than 15 km. These values indicate a clear change in slope around 15 km as suggested by Singer et al. (2019) and thus independently confirm their conclusions. Our slopes however are both slightly flatter than those found more recently by Robbins and Singer (2021). Our trained models and relevant codes are available online on github.com/malidib/ACID .