  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 .
