WeChat Mini Program
Old Version Features

    • Capture Efficiency Analysis in the Circular Restricted Three-Body Problem

    Yu-Xuan Miao,Xi-Yun Hou

    Research in Astronomy and Astrophysics（2024）

    Nanjing Univ

    Cited 0|Views9
    Abstract
    Temporary capture efficiency is studied in the framework of the circular restricted three-body problem in two steps. First, a non-uniform distribution of test particles around the secondary's orbit is obtained by fully accounting the secondary's gravitational influence. Second, the capture efficiency is computed based on the non-uniform distribution. Several factors influencing the result are discussed. By studying the capture efficiency in the circular restricted three-body problem of different mass ratios, a power-law relation between the capture efficiency (p) and the mass ratio (mu) is established, which is given by p approximate to 0.27 x mu(0.53), within the range of 3.0035 x 10(-6) <= mu <= 3.0034 x 10(-5). Taking the Sun-Earth system as an example, the influence from the orbit eccentricity of the secondary on the non-uniform distribution and the capture efficiency is studied. Our studies find that the secondary's orbit eccentricity has a negative influence on the capture efficiency.
    More
    Translated text
    Key words
    methods: numerical,celestial mechanics,planets and satellites: dynamical evolution and stability
    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