用深度学习直接从光变曲线反推小行星形状，速度快且更准。
Asteroid shape inversion with light curves using deep learning
- 用神经网络建立光变曲线与形状的直接映射，跳过迭代计算。
- 预测非凸形状凹陷区域的交并比达0.89，精度高。
- 适用于特殊形状小行星，适合天文观测数据处理人员。
小行星形状反演是行星科学和天文学研究的关键领域。然而，现有方法依赖大量迭代计算，耗时且易陷入局部最优。本文通过深度神经网络直接建立光度数据与形状分布之间的映射关系，并采用3D点云表示小行星形状，利用非凸小行星与其凸包的光变曲线差异来预测凹陷区域。通过传统方法与本方法在形状模型上的切比雪夫距离对比发现，本方法表现更优，尤其在处理特殊形状时。对凸包上凹陷区域的检测，预测的交并比（IoU）达到0.89。进一步使用洛厄尔天文台观测数据，对小行星3337 Milo和1289 Kuta进行凸形预测，并开展光变曲线拟合实验。结果验证了该方法的鲁棒性与适应性。
原文摘要 · Abstract (English)
Asteroid shape inversion using photometric data has been a key area of study in planetary science and astronomical research.However, the current methods for asteroid shape inversion require extensive iterative calculations, making the process time-consuming and prone to becoming stuck in local optima. We directly established a mapping between photometric data and shape distribution through deep neural networks. In addition, we used 3D point clouds to represent asteroid shapes and utilized the deviation between the light curves of non-convex asteroids and their convex hulls to predict the concave areas of non-convex asteroids. We compared the results of different shape models using the Chamfer distance between traditional methods and ours and found that our method performs better, especially when handling special shapes. For the detection of concave areas on the convex hull, the intersection over union (IoU) of our predictions reached 0.89. We further validated this method using observational data from the Lowell Observatory to predict the convex shapes of the asteroids 3337 Milo and 1289 Kuta, and conducted light curve fitting experiments. The experimental results demonstrated the robustness and adaptability of the method
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