César Fuentes | Searcharxiv

arXiv2026

1I/'Oumuamua-like objects's SFDs with older, current and future surveys

Interstellar Objects (ISOs) may provide direct evidence of planetesimals ejected from early-stage solar systems. Studying these objects and predicting their detection through various surveys is vital for enhancing our understanding of planetary formation. We show how ISO size distribution (SFD), f_D~D^-q, controls expected detections in current and future surveys. We focus on TESS and the Vera Rubin Observatory LSST while analyzing Pan-STARRS and ATLAS. TESS is expected to detect ~1 ISO from shallow SFDs (q<2.5). On the other hand, LSST is likely to detect ~0.2-100 ISO yr^-1 over its lifetime. ATLAS is likely to detect one non-cometary object for populations with q<3. We further estimate ~0-8 detections of cometary like-3I/ATLAS objects by LSST in 10 years and ~1 cometary object detected by TESS over its mission time, independent of the nucleus size distribution. By considering both 1I/'Oumuamua and 3I/ATLAS from the same SFD, our results suggest that more asteroid-like objects are produced rather than comets during the early stages of planetary formation. Imminent detections of ISOs by LSST will help constrain the slopes of the SFDs for asteroid-like and cometary ISOs better, and, if applicable, differentiate between them.

astro-ph.EP↗

arXiv2026

You Only Stack Once (YOSO): A Motion-Filtered, Deep-Learning Framework for Detecting Faint Moving Sources

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.

astro-ph.EP↗