论文标题
Charon的陨石坑的机器生成的目录及其对Kuiper带的影响
A machine-generated catalogue of Charon's craters and implications for the Kuiper belt
论文作者
论文摘要
在本文中，我们使用深度学习模型调查了Charon的Craters尺寸分布。这是由Singer等人最近的结果动机。 （2019年）使用手动编目发现，直径小于12 km的山火车的尺寸分布斜率发生了变化，转化为小型kuiper带对象的稀少。罗宾斯和歌手（2021）证实了这些结果，但莫比德利等人反对。 （2021），需要进行独立审查。我们基于MaskRCNN的模型集合在Lunar，Mercurian和Martian火山口目录以及光学和数字高程图像上进行了培训。我们使用强大的图像增强方案来迫使模型概括并转移到冰冷的对象中。在没有先前的偏见或接触Charon的情况下，我们的模型发现小于10 km的陨石坑的Q = -1.47+-0.33的最佳拟合斜率，而大于15 km的山口温s = -2.91+-0.51。这些值表明，如Singer等人所建议的那样，斜率明显变化。 （2019年），因此独立确认了他们的结论。然而，我们的斜坡都比Robbins和Singer（2021）最近发现的斜坡稍微更平坦。我们训练有素的模型和相关代码可在github.com/malidib/acid上在线获得。
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 .
