发表机构
University of Pisa（比萨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文比较了数据驱动与物理信息神经网络在近地天体极短弧初始轨道确定中的性能，物理信息模型在可接受性上更优，尤其对快速近距离弧具有优势。
AI 中文摘要
从极短弧（VSAs）进行初始轨道确定（IOD）仍然是小行星监测和天体力学中最具挑战性的开放问题之一。经典方法需要覆盖轨道足够部分的角观测，当只有单个观测夜晚可用时（大多数新发现的近地天体（NEOs）即属此类），这些方法会变得病态或直接失败。我们提出了两种直接针对测距问题的神经网络（NN）模型的比较分析：两种模型均输入来自单个VSA的三个带时间标记的角测量值$(t_i,\alpha_i,\delta_i)$，$i=1,2,3$，并预测地心距离和距离变化率$(\rho,\dot\rho)$，从而完成轨道状态向量。第一种是在纯数据驱动目标上训练的多层感知器；第二种则在其基础上增加了物理信息损失。两者均在对象不相交划分下，使用NEODyS-2目录中$39\\,031$个真实NEOs的$568\\,127$个VSAs样本进行训练。每个模型按构造对每条弧返回估计值，因此我们转而评估该估计是否动态可接受：在包含$56\\,773$条弧的保留测试集上，物理信息模型将其预测的$85.1\\%$置于可接受区域内，而数据驱动基线为$76.5\\%$，相比之下，高斯和拉普拉斯方法仅在相同弧的$45.0\\%$和$47.3\\%$上返回解，并且在约十分之九的情况下坍缩到退化根。对自行和真实距离的分层分析表明，物理信息目标并不统一优于基线：它以更长的误差尾部换取了可接受性以及在快速、近距离弧上的显著优势，而这些弧在操作上最为相关。
英文摘要
Initial Orbit Determination (IOD) from Very Short Arcs (VSAs) remains one of the most challenging open problems in asteroid surveillance and celestial mechanics. Classical methods require angular observations spanning a sufficient fraction of the orbit, and become ill-conditioned or fail outright when only a single observing night is available, as is the case for most newly discovered Near-Earth Objects (NEOs). We present a comparative analysis of two Neural Network (NN) models that attack the ranging problem directly: both ingest a triplet of time-tagged angular measurements $(t_i,α_i,δ_i)$, $i=1,2,3$, from a single VSA and predict the geocentric range and range-rate $(ρ,\dotρ)$, thus completing the orbital state vector. The first is a Multi-Layer Perceptron trained on a purely data-driven objective; the second augments it with a physics-informed loss. Both are trained, under an object-disjoint partition, on a sample of the $568\,127$ VSAs of $39\,031$ real NEOs available in the NEODyS-2 catalogue. Each model returns an estimate on every arc by construction, so we assess instead whether that estimate is dynamically admissible: on a held-out test set of $56\,773$ arcs the physics-informed model places $85.1\%$ of its predictions inside the admissible region, against $76.5\%$ for the data-driven baseline, whereas Gauss's and Laplace's methods return a solution on only $45.0\%$ and $47.3\%$ of the same arcs and collapse onto the degenerate root in about nine of those cases out of ten. A stratified analysis over proper motion and true range shows that the physics-informed objective is not uniformly superior to the baseline: it trades a longer error tail for admissibility and for a marked advantage on the fast, nearby arcs that are operationally the most relevant.
Comments27 pages, 8 figures
