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arXiv 2301.09375astro-ph.EPastro-ph.GAastro-ph.IM
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太阳系中星际天体的合成布居

Synthetic Population of Interstellar Objects in the Solar System

Dušan Marčeta

摘要:

前两颗穿越太阳系的宏观星际天体(ISO)的发现为行星科学开辟了全新的视角。对这些天体的探索使人们对整个银河系行星系的起源、结构和演变过程有了质的新认识。如果当前和未来的巡天观测发现更多的ISO,对这些现象的了解将大大提高。另一方面,调查需要更好地描述这一群体的特征,以改进其发现算法。然而,尽管它们具有科学意义,但仍然没有太阳系中ISO的综合轨道模型和计算效率高的算法来生成它们的合成表示,以满足这些日益增长的需求。目前产生合成种群的方法不能完全考虑到重要的现象,如引力聚焦和太阳的屏蔽效应。另一方面,这种方法在计算上也要求太高,无法用于ISO人口的系统探索。本文提出了一种基于多元反变换抽样方法的确定ISOs轨道根数分布的解析方法,以及计算效率高的生成ISOs合成总体的算法。所开发的方法是几个数量级更有效的比现有的方法,这取决于合成人口的大小。该方法的Python实现是免费提供的,可用于生成具有用户定义的输入参数的ISO的合成群体。

英文摘要:

The discovery of the first two macroscopic interstellar objects (ISOs) passing through the Solar System has opened entirely new perspectives in planetary science. The exploration of these objects offers a qualitatively new insight into the processes related to the origin, structure and evolution of planetary systems throughout the Galaxy. Knowledge about these phenomena will greatly advance if current and future sky surveys discover more ISOs. On the other hand, the surveys require better characterization of this population in order to improve their discovery algorithms. However, despite their scientific significance, there is still no comprehensive orbital model of ISOs in the Solar System and computationally efficient algorithm for generating their synthetic representations that would respond to these increasing needs. Currently available method for generating synthetic populations cannot fully take into account important phenomena, such as gravitational focusing and the shielding effect of the Sun. On the other hand, this method is also computationally far too demanding to be used for systematic exploration of the ISO population. This paper presents an analytical method for determining the distributions of the orbital elements of ISOs, as well as computationally efficient algorithm for generating their synthetic populations, based on the multivariate inverse transform sampling method. The developed method is several orders of magnitudes more efficient than the available method, depending on the size of the synthetic population. A Python implementation of the method is freely available and can be used to generate synthetic populations of ISOs with user-defined input parameters.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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