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

TL;DR

This study uses a deep learning model to analyze Charon's crater size distribution, confirming a slope change around 15 km and providing an independent validation of previous findings about Kuiper Belt objects.

Contribution

Introduces a MaskRCNN-based ensemble model trained on planetary crater data to analyze Charon's craters without prior bias, confirming the slope change in crater size distribution.

Findings

Confirmed a slope change around 15 km in crater sizes.

Found slightly flatter slopes than recent studies.

Provided open-source code and trained models for further research.

Abstract

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 .