YOSO: Deep Learning Pipeline Enhances Detection of Faint Solar System Objects

The You Only Stack Once (YOSO) pipeline represents a significant advancement in the field of astronomical object detection, particularly for faint and slow-moving objects within our solar system. By integrating a Gaussian Motion Filter (GMoF), YOSO effectively amplifies the signal-to-noise ratio of objects exhibiting specific motion patterns, distinguishing them from random noise and static background features. This approach offers a distinct advantage over conventional shift-and-stack methods, which rely on discrete velocity trials and can be computationally expensive. The successful application of YOSO to DEEP observations from the Dark Energy Camera, resulting in the recovery of previously detected objects and the discovery of new Trans-Neptunian Objects (TNOs) and near-Solar System objects, underscores its effectiveness. Furthermore, the pipeline's low false positive rate ensures that detected objects are highly likely to be genuine, reducing the need for extensive follow-up observations. The potential deployment of YOSO on large-scale surveys like LSST and its adaptability to other domains, such as exoplanet imaging and NEO detection, highlight its versatility and scalability. However, it is important to acknowledge that YOSO's sensitivity is slightly lower than alternative methods, which may result in the omission of some of the faintest objects. Future research should focus on optimizing the pipeline's sensitivity while maintaining its low false positive rate to maximize its overall effectiveness in astronomical object detection.

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