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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
