You Only Stack Once (YOSO): A Motion-Filtered, Deep-Learning Framework for Detecting Faint Moving Sources
Nitya Pandey, C\'esar Fuentes, Pedro Bernardinelli, Valeria Fr\'ias, Colin Orion Chandler, David E. Trilling, Matthew J. Holman, Steven Stetzler, Dallin Spencer, Hsing Wen Lin, Luis E. Salazar Manzano, Darin Ragozzine, Ryder Strauss, Mario Juri\'c, Andrew J. Connolly

TL;DR
YOSO is a novel deep-learning pipeline that uses a Gaussian Motion Filter to detect faint, slow-moving objects in astronomical surveys with high accuracy and low false positives.
Contribution
It introduces GMoF, a pixel-level filter that enhances trails of moving objects, outperforming traditional methods in sensitivity and false positive reduction.
Findings
Recovered 45 of 73 known objects in DEEP data
Discovered 11 new TNOs and 216 near-Solar System objects
Achieved lower false positive rate compared to shift-and-stack methods
Abstract
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…
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