论文标题
通过致密性来改善影响监测
Improving impact monitoring through Line Of Variations densification
论文作者
论文摘要
我们提出了一种致密化算法，以改善撞击监视的变化线（LOV）方法，当信息太少时可能会失败，因为在困难情况下可能会发生这种情况。 LOV方法使用一维抽样来探索小行星的不确定性区域。样品轨道的接近方法按时间和LOV索引分组，以形成所谓的回报，并分析每个回报以搜索沿着LOV距地球距离距离的局部最小值。问题的强烈非线性导致回报的发生，因此可以预防成功的分析。我们的致密算法试图以5分的回报最多转换返回最多的返回，将新的点添加到原始返回中。由于LOV的复杂演变，不一定立即实现此操作：在这种情况下，利用了有关第一次尝试的LOV几何形状的信息进行进一步尝试。最后，我们提供了一些示例，表明我们方法的应用可能会对影响监视结果产生重大影响，特别是关于虚拟影响器搜索的完整性。
We propose a densification algorithm to improve the Line Of Variations (LOV) method for impact monitoring, which can fail when the information is too little, as it may happen in difficult cases. The LOV method uses a 1-dimensional sampling to explore the uncertainty region of an asteroid. The close approaches of the sample orbits are grouped by time and LOV index, to form the so-called returns, and each return is analysed to search for local minima of the distance from the Earth along the LOV. The strong non-linearity of the problem causes the occurrence of returns with so few points that a successful analysis can be prevented. Our densification algorithm tries to convert returns with length at most 3 in returns with 5 points, properly adding new points to the original return. Due to the complex evolution of the LOV, this operation is not necessarily achieved all at once: in this case the information about the LOV geometry derived from the first attempt is exploited for a further attempt. Finally, we present some examples showing that the application of our method can have remarkable consequences on impact monitoring results, in particular about the completeness of the virtual impactors search.
