Celletti Alessandra, Vartolomei Tudor
Department of Mathematics, University of Roma Tor Vergata, Via della Ricerca Scientifica 1, 00133, Roma, Italy.
Faculty of Mathematics, Al. I. Cuza University, Bd. Carol I 11, 700506, Iasi, Romania.
Sci Rep. 2025 May 23;15(1):17925. doi: 10.1038/s41598-025-02434-9.
Space debris around the Earth are becoming an increasing threat for space missions. Their number is growing due to the frequent launches of satellites from space agencies and private enterprises. This study examines simulated break-up events alongside actual samples of catastrophic events; we analyse the generated fragments with the objective of assigning them to clusters and classifying the debris based on their dynamical properties. We propose to accomplish these goals by performing the analysis using the so-called proper elements, which are quantities obtained by implementing perturbation theory to average the equations of motion over the angle variables. Subsequent to this filtering procedure, the proper elements enjoy the remarkable property to remain nearly constant over time. We find that proper elements are highly suitable for the analysis through machine learning methods with the purposes of clustering and classifying the fragments.
地球周围的空间碎片对太空任务构成的威胁日益增加。由于太空机构和私营企业频繁发射卫星，其数量正在不断增长。本研究考察了模拟解体事件以及灾难性事件的实际样本；我们分析所产生的碎片，目的是将它们归类成群组，并根据其动力学特性对碎片进行分类。我们建议通过使用所谓的特征元素来进行分析以实现这些目标，特征元素是通过应用微扰理论对角度变量上的运动方程进行平均而得到的量。经过此过滤程序后，特征元素具有随时间几乎保持不变的显著特性。我们发现，特征元素非常适合通过机器学习方法进行分析，以便对碎片进行聚类和分类。
