WeChat Mini Program
Old Version Features

    • Crater Dimensions on the Dwarf Planets

    Veronica J. Bray, Michael F. Zeilnhofer,S. J. Robbins,P. M. Schenk, Kelsi M. Singer,O. L. White,S. A. Stern,S. D. Benecchi, K. A. Durfey,H. A. Weaver,C. B. Olkin,J. R. Spencer

    PLANETARY SCIENCE JOURNAL（2025）

    Univ Arizona

    Cited 0|Views2
    Abstract
    The icy dwarf planets of Pluto, Charon, and Ceres provide important geologic comparisons for each other as they possess similar gravities and experience impacts of similar velocities compared to larger planets and moons. Differences in their impact craters can therefore be attributed to differences in the impactor and/or target compositions. Craters on Ceres show taller rims than craters on Charon, but comparable internal crater dimensions (depth, wall slope, and peak size). It is possible that the non-ice component of the Ceres crust provides additional strength to this outer portion of the crater, maintaining less rim collapse during the modification stage of crater formation, while the warmer central crater region is controlled by the weakness of the icy component. The spreads in depth-to-diameter ratio and wall-slope values for the Pluto data set are not clearly related to preservation state, suggesting that fresh craters on Pluto possess a large variance in these crater dimensions compared to Charon. This could be due to the geologically diverse terrains and depositional histories at different latitudes and longitudes of Pluto. These provide different target properties for each impact, so that even pristine impact craters on Pluto could display a wide range in depths and wall slopes based on the different strength of the target in each area.
    More
    Translated text
    Key words
    Craters,Ceres,Plutonian satellites,Pluto
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined