论文标题
使用卷积神经网络和基于模拟的推理，轻巧的星际位置感测
Lightweight starshade position sensing with convolutional neural networks and simulation-based inference
论文作者
论文摘要
《星际》是一项领先的技术，可实现地球样系外行星的直接检测和光谱表征。为了使星胸和望远镜在较大的分离上对齐，需要可靠地感测遮挡恒星的衍射光的峰。当前技术依赖于图像匹配或模型拟合，这两者都在资源有限的航天器计算机上施加了实质性的计算负担。我们提出了一种基于卷积神经网络与基于模拟的推理技术配对的轻量级图像处理方法，以估计Arago的位置及其不确定性。该方法在整个学生平面上达到了几厘米的准确性，而在测试时，每个图像中只需要1.6 MB，并且在存储的数据结构和5.3 Mflops（百万浮点操作）中只需要1.6 MB。通过在普林斯顿星际测试床上部署我们的方法，我们证明了神经网络可以在模拟图像上进行训练并在真实图像上使用，并且可以成功地集成在控制系统中以进行闭环编队飞行。
Starshades are a leading technology to enable the direct detection and spectroscopic characterization of Earth-like exoplanets. To keep the starshade and telescope aligned over large separations, reliable sensing of the peak of the diffracted light of the occluded star is required. Current techniques rely on image matching or model fitting, both of which put substantial computational burdens on resource-limited spacecraft computers. We present a lightweight image processing method based on a convolutional neural network paired with a simulation-based inference technique to estimate the position of the spot of Arago and its uncertainty. The method achieves an accuracy of a few centimeters across the entire pupil plane, while only requiring 1.6 MB in stored data structures and 5.3 MFLOPs (million floating point operations) per image at test time. By deploying our method at the Princeton Starshade Testbed, we demonstrate that the neural network can be trained on simulated images and used on real images, and that it can successfully be integrated in the control system for closed-loop formation flying.
