YOSO用像素级运动滤波,高效发现微弱移动天体
You Only Stack Once (YOSO): A Motion-Filtered, Deep-Learning Framework for Detecting Faint Moving Sources

- 采用像素级高斯运动滤波,动态增强移动信号
- 在DEEP数据中找回73个已知天体中的45个,发现11个新冥王星外天体
- 适合大尺度巡天与近地小行星探测,误报率极低
我们提出You Only Stack Once (YOSO),一种用于宽视场天文巡天中检测微弱慢速移动太阳系天体的自动化流程。该流程集成新型像素级高斯运动滤波器(GMoF),能有效提升不同运动速率目标的信噪比。与传统移位叠加方法不同,GMoF在不依赖离散速度试探的前提下,强化轨迹信号并抑制随机噪声与静态背景。在暗能量相机(Dark Energy Camera)的DEEP观测子集上,YOSO成功恢复了73个已知天体中的45个,并发现了11个新的海王星外天体(TNOs)及216个近太阳系天体。尽管其他移位叠加方法可探测约0.88星等更暗的目标,但YOSO因仅识别具有轨迹特征且在正确速度下可对齐为点源的信号,故误报率极低。本方法可扩展至大规模巡天如LSST,并适用于角差成像(ADI)和近地天体(NEO)探测等需运动增强信号的领域,为数据密集型天文时代提供通用、可扩展的微弱运动信号提取方案。
原文摘要 · Abstract (English)
We present You Only Stack Once (YOSO), an automated pipeline designed to detect faint, slow-moving Solar System objects in wide-field astronomical surveys. The pipeline integrates a novel Gaussian Motion Filter (GMoF) that operates at the pixel level to enhance signal-to-noise for objects exhibiting a range of apparent rates of motion. Unlike conventional shift-and-stack methods, which rely on discrete velocity trials, GMoF amplifies trails while suppressing random noise and static background features. Applied to a subset of DEEP observations from the Dark Energy Camera, YOSO recovered 45 out of 73 previously detected objects, as well as 11 new TNOs. It also discovered 216 objects in the near Solar System. Although alternative shift-and-stack methods are sensitive to objects about 0.88 magnitudes fainter, YOSO's false positive rate is extremely low, since it detects only sources that exhibit a trail and are consistent with a point source when shifted at the right rate. We show how this method can be deployed on large surveys like LSST, and adapted for other domains that require motion-based signal enhancement, including exoplanet imaging through Angular Differential Imaging (ADI), and near-Earth object (NEO) detection for missions like NEO Surveyor. YOSO thus provides a versatile, scalable approach for extracting faint, motion-dependent signals in the era of data-intensive astronomy.
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