从静态中筛选：差分图像中的运动目标检测
Sifting Through the Static: Moving Object Detection in Difference Images
摘要：
海王星外天体（TNOs）提供了一个了解太阳系历史的窗口，但由于它们与太阳的距离和相对较低的亮度，观察它们可能具有挑战性。在这里，我们报告了75个移动物体的检测，我们不能链接到任何其他已知的对象，其中最微弱的VR幅度$25.02 \pm 0.93$使用KBMOD平台。我们恢复了额外的24个来源与以前已知的轨道。我们对这些物体的重心距离、倾角和升交点经度进行了约束。这些不明物体的重心距离中值为41.28天文单位，位于外太阳系。观测到的倾斜度和星等分布的所有检测到的对象是一致的，与以前公布的KBO分布。我们描述了KBMOD的扩展，包括一个强大的基于纹理的光曲线过滤器，一个内嵌的图形处理单元（GPU）过滤器，新的coadded邮票生成，和一个卷积神经网络（CNN）邮票过滤器，它允许KBMOD利用差分图像。这些改进标志着KBMOD在部署未来大数据调查（如LSST）方面的准备程度有了显著提高。
英文摘要：
Trans-Neptunian Objects (TNOs) provide a window into the history of the Solar System, but they can be challenging to observe due to their distance from the Sun and relatively low brightness. Here we report the detection of 75 moving objects that we could not link to any other known objects, the faintest of which has a VR magnitude of $25.02 \pm 0.93$ using the KBMOD platform. We recover an additional 24 sources with previously-known orbits. We place constraints on the barycentric distance, inclination, and longitude of ascending node of these objects. The unidentified objects have a median barycentric distance of 41.28 au, placing them in the outer Solar System. The observed inclination and magnitude distribution of all detected objects is consistent with previously published KBO distributions. We describe extensions to KBMOD, including a robust percentile-based lightcurve filter, an in-line graphics processing unit (GPU) filter, new coadded stamp generation, and a convolutional neural network (CNN) stamp filter, which allow KBMOD to take advantage of difference images. These enchancements mark a significant improvement in the readiness of KBMOD for deployment on future big data surveys such as LSST.
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