five

General relativistic precession and the long-term stability of the solar system: SimulationArchive dataset

收藏
NIAID Data Ecosystem2026-05-01 收录
下载链接:
https://zenodo.org/record/7765187
下载链接
链接失效反馈
官方服务:
资源简介:
We share the data for 1280 long-term solar system simulations used in Brown & Rein (2023). The simulations are zipped together consecutively in groups of 16 and saved in the REBOUND (3.18.1) SimulationArchive format. Please see the companion paper and code for details. Abstract The long-term evolution of the solar system is chaotic. In some cases, chaotic diffusion caused by an overlap of secular resonances can increase the eccentricity of planets when they enter into a linear secular resonance, driving the system to instability. Previous work has shown that including general relativistic contributions to the planets' precession frequency is crucial when modelling the solar system. It reduces the probability that the solar system destabilizes within 5 Gyr by a factor of 60. We run 1280 additional N-body simulations of the solar system spanning 12.5 Gyr where we allow the general relativistic precession rate to vary with time. We develop a simple, unified, Fokker-Planck advection-diffusion model that can reproduce the instability time of Mercury with, without, and with time-varying general relativistic precession. We show that while ignoring general relativistic precession does move Mercury's precession frequency closer to a resonance with Jupiter, this alone does not explain the increased instability rate. It is necessary that there is also a significant increase in the rate of diffusion. We find that the system responds smoothly to a change in the precession frequency: There is no critical general relativistic precession frequency below which the solar system becomes significantly more unstable. Our results show that the long-term evolution of the solar system is well described with an advection-diffusion model.
创建时间:
2023-04-06
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作