用神经隐式模型快速高精度预测小天体遮挡事件
EclipseNETs: Learning Irregular Small Celestial Body Silhouettes
- 用神经网络建模小天体不规则轮廓，替代传统射线追踪
- 在4个真实天体上达到与射线追踪相当精度，速度提升数个数量级
- 仅需稀疏轨迹数据即可训练，适合缺乏精确形状模型的场景
精确预测不规则小天体周围的日食事件对航天器导航、轨道确定和系统管理至关重要。本文提出一种新方法，利用神经隐式表示高效可靠地建模日食条件。我们设计的神经网络架构能以高精度捕捉小行星和彗星的复杂轮廓。在四个已知特性明确的天体——本努（Bennu）、伊藤川（Itokawa）、67P/楚留莫夫-格拉希门克（67P/Churyumov-Gerasimenko）和爱罗斯（Eros）上测试，该方法精度与传统射线追踪相当，但速度提升数个数量级。此外，我们开发了一种间接学习框架，通过神经常微分方程直接从稀疏轨迹数据中训练模型，无需预先知道精确形状模型。该方法可随新轨迹数据持续优化，逐步降低误差并提升预测精度。
原文摘要 · Abstract (English)
Accurately predicting eclipse events around irregular small bodies is crucial for spacecraft navigation, orbit determination, and spacecraft systems management. This paper introduces a novel approach leveraging neural implicit representations to model eclipse conditions efficiently and reliably. We propose neural network architectures that capture the complex silhouettes of asteroids and comets with high precision. Tested on four well-characterized bodies - Bennu, Itokawa, 67P/Churyumov-Gerasimenko, and Eros - our method achieves accuracy comparable to traditional ray-tracing techniques while offering orders of magnitude faster performance. Additionally, we develop an indirect learning framework that trains these models directly from sparse trajectory data using Neural Ordinary Differential Equations, removing the requirement to have prior knowledge of an accurate shape model. This approach allows for the continuous refinement of eclipse predictions, progressively reducing errors and improving accuracy as new trajectory data is incorporated.
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