近地小行星轨道误差的统计分析  

Statistical Analysis of Orbital Uncertainty of Near-Earth Asteroids

作　　者：王秀海 胡寿村 赵海斌[1,3] WANG Xiu-hai;HU Shou-cun;ZHAO Hai-bin(Key Laboratory of Planetary Sciences,Purple Mountain Observatory,Chinese Academy of Sciences,Nanjing 210023;School of Astronomy and Space Science,University of Science and Technology of China,Hefei 230026;Center for Excellence in Comparative Planetology,Chinese Academy of Sciences,Hefei 230026)

机构地区：[1]中国科学院紫金山天文台行星科学重点实验室,南京210023 [2]中国科学技术大学天文与空间科学学院,合肥230026 [3]中国科学院比较行星学卓越创新中心,合肥230026

出　　处：《天文学报》2024年第5期59-69,共11页Acta Astronomica Sinica

基　　金：国家自然科学基金项目(62227901);中国科学院战略性先导科技专项(XDB41000000);空间碎片与近地小行星防御科研项目(KJSP2020020204、KJSP2020020102);中国载人航天工程巡天空间望远镜科学专项(CMS-CSST-2021-B08);小行星基金会资助。

摘　　要：精确确定近地小行星轨道是近地天体监测预警工作的重要部分之一,然而由于观测弧长、观测精度以及力模型的制约,不同目标存在不同的轨道误差,基于JPL(Jet Propulsion Laboratory)小天体数据库中3万多颗近地小行星的轨道数据,对其轨道根数误差开展了统计分析研究。发现近地小行星轨道半长径误差存在双峰分布现象,且产生的原因与观测弧段的分布有关。研究了半长径误差与观测弧长的关系,得到了拟合优度达0.90的回归方程,进一步将绝对星等参数考虑在内,采用BP(Back Propagation)神经网络训练方法对观测始末时间跨度、轨道周期、绝对星等。半长径误差搭建了参数训练网络,该方法使得拟合优度进一步提升至0.96,能够快速合理地对近地小行星半长径误差进行评估。此外,对比了观测弧长对半长径误差、偏心率误差、倾角误差的影响,发现3者随观测弧长增加的改进程度存在差异,最后,还对比分析了半长径与轨道倾角误差分布随轨道倾角的变化,发现倾角误差分布特征与观测精度的选择效应有关。这些统计分析工作有助于增加对近地小行星轨道误差分布规律的认识,为进一步改进近地小行星轨道精度提供参考。Precisely determining the orbit of near-Earth asteroid(NEA)is one of the important parts of the near-Earth objects monitoring and early warning.However,due to the constraints of the observation arc length,observation accuracy and force model,different objects have different orbit errors.Based on the orbit data of more than 30000 near-Earth asteroids in JPL(Jet Propulsion Laboratory)small body database,the orbital elements error can be analyzed statistically.It is found that the semi-major axis error of the NEAs orbit has a bimodal distribution,and the reason is related to the distribution of the observation arc.The relationship between semi-major axis error and observation arc length is studied,and a regression equation with a goodness of fit of 0.90 is obtained.Furthermore,taking the absolute magnitude parameter into account,BP(Back Propagation)neural network training method is used to build a parameter training network for the beginning and end time span of observation,orbit period,absolute magnitude and semi-major axis error.The proposed method further improves the goodness of fit to 0.96,which can be used to quickly and reasonably evaluate the semi-major axis error of near-Earth asteroids.In addition,the influence of the observation arc length on the semi-major axis error,eccentricity error and inclination error is compared,and it is found that the improvement degree of the three is different with the increase of the observation arc length.Finally,the variation of the distribution of semi-major axis and orbit inclination error with orbit inclination is compared and analyzed,and it is found that the distribution characteristics of inclination error are related to the selection effect of observation accuracy.These statistical analyses contribute to a better understanding of the distribution of NEA orbit errors,and provide reference for further improvements in the orbital accuracy.

关 键 词：小天体:近地小行星 天体力学:轨道误差 方法:统计 

分 类 号：P135[天文地球—天体力学]