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A machine-generated catalogue of Charon’s craters and implications for the Kuiper belt

Presentation #401.02 in the session Numerics and Methods for Planetary Dynamics.

Published onApr 25, 2022
A machine-generated catalogue of Charon’s craters and implications for the Kuiper belt

We investigate Charon’s craters size distribution using a deep learning model. This is motivated by the recent results of Singer+2020 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. 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 D-1.28 ± 0.026 for craters smaller than 11 km, and D-2.22 ± 0.039 for larger craters. These values are consistent with Singer and thus independently confirm their conclusions.


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