You Only Stack Once (YOSO): A Motion-Filtered, Deep-Learning Framework for Detecting Faint Moving Sources
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| author | Pandey, Nitya Fuentes, César Bernardinelli, Pedro Frías, Valeria Chandler, Colin Orion Trilling, David E. Holman, Matthew J. Stetzler, Steven Spencer, Dallin Lin, Hsing Wen Manzano, Luis E. Salazar Ragozzine, Darin Strauss, Ryder Jurić, Mario Connolly, Andrew J. Smotherman, Hayden Sheppard, Scott S. Napier, Kevin |
| author_facet | Pandey, Nitya Fuentes, César Bernardinelli, Pedro Frías, Valeria Chandler, Colin Orion Trilling, David E. Holman, Matthew J. Stetzler, Steven Spencer, Dallin Lin, Hsing Wen Manzano, Luis E. Salazar Ragozzine, Darin Strauss, Ryder Jurić, Mario Connolly, Andrew J. Smotherman, Hayden Sheppard, Scott S. Napier, Kevin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_06913 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | You Only Stack Once (YOSO): A Motion-Filtered, Deep-Learning Framework for Detecting Faint Moving Sources Pandey, Nitya Fuentes, César Bernardinelli, Pedro Frías, Valeria Chandler, Colin Orion Trilling, David E. Holman, Matthew J. Stetzler, Steven Spencer, Dallin Lin, Hsing Wen Manzano, Luis E. Salazar Ragozzine, Darin Strauss, Ryder Jurić, Mario Connolly, Andrew J. Smotherman, Hayden Sheppard, Scott S. Napier, Kevin Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | You Only Stack Once (YOSO): A Motion-Filtered, Deep-Learning Framework for Detecting Faint Moving Sources |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2605.06913 |