An Efficient Shift-and-stack Algorithm Applied to Detection Catalogs

Abstract The boundary of solar system object discovery lies in detecting its faintest members. However, their discovery in detection catalogs from imaging surveys is fundamentally limited by the practice of thresholding detections at signal-to-noise (SNR) ≥ 5 to maintain catalog purity. Faint moving objects can be recovered from survey images using the shift-and-stack algorithm, which coadds pixels from multi-epoch images along a candidate trajectory. Trajectories matching real objects accumulate signal coherently, enabling high-confidence detections of very faint moving objects. Applying shift-and-stack comes with high computational cost, which scales with target object velocity, typically limiting its use to searches for slow-moving objects in the outer solar system. This work introduces a modified shift-and-stack algorithm that trades sensitivity for speedup. Our algorithm stacks low-SNR detection catalogs instead of pixels, the sparsity of which enables approximations that reduce the number of stacks required. Our algorithm achieves real-world speedups of 10–10 3 × over image-based shift-and-stack while retaining the ability to find faint objects. We validate its performance by recovering synthetic inner and outer solar system objects injected into images from the DECam Ecliptic Exploration Project. Exploring the sensitivity–compute time trade-off of this algorithm, we find that our method achieves a speedup of ∼30× with 88% of the memory usage while sacrificing 0.25 mag in depth compared to image-based shift-and-stack. These speedups enable the broad application of shift-and-stack to large-scale imaging surveys and searches for faint inner solar system objects. We provide a reference implementation via the find-asteroids Python package and this URL: https://github.com/stevenstetzler/find-asteroids .

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Publication Details

Journal
The Astronomical Journal
Published
2025-11-26
DOI
https://doi.org/10.3847/1538-3881/ae0e1a
Primary Topic
Astro and Planetary Science
Type
article
Field-Weighted Citation Impact
0.00

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article

An Efficient Shift-and-stack Algorithm Applied to Detection Catalogs

Hsing Wen Lin, Matthew J. Payne, Michael Mommert, Kevin J. Napier et al.
The Astronomical Journal
Astro and Planetary Science
article

An Efficient Shift-and-stack Algorithm Applied to Detection Catalogs

Hsing Wen Lin, Matthew J. Payne, Michael Mommert, Kevin J. Napier, Luis E. Salazar Manzano, Steven Stetzler, D. W. Gerdes, Fred C. Adams, Mario Jurić, David E. Trilling, Andrew J. Connolly, D. Spencer, Larissa Markwardt, Chadwick A. Trujillo, Scott S. Sheppard, Ryder Strauss, Matthew J. Holman, Dino Bektešević, A. S. Rivkin, César Fuentes, William J. Oldroyd, Pedro H. Bernardinelli, Colin Orion Chandler, Andrew McNeill, Hilke E. Schlichting
article en

Abstract

Abstract The boundary of solar system object discovery lies in detecting its faintest members. However, their discovery in detection catalogs from imaging surveys is fundamentally limited by the practice of thresholding detections at signal-to-noise (SNR) ≥ 5 to maintain catalog purity. Faint moving objects can be recovered from survey images using the shift-and-stack algorithm, which coadds pixels from multi-epoch images along a candidate trajectory. Trajectories matching real objects accumulate signal coherently, enabling high-confidence detections of very faint moving objects. Applying shift-and-stack comes with high computational cost, which scales with target object velocity, typically limiting its use to searches for slow-moving objects in the outer solar system. This work introduces a modified shift-and-stack algorithm that trades sensitivity for speedup. Our algorithm stacks low-SNR detection catalogs instead of pixels, the sparsity of which enables approximations that reduce the number of stacks required. Our algorithm achieves real-world speedups of 10–10 3 × over image-based shift-and-stack while retaining the ability to find faint objects. We validate its performance by recovering synthetic inner and outer solar system objects injected into images from the DECam Ecliptic Exploration Project. Exploring the sensitivity–compute time trade-off of this algorithm, we find that our method achieves a speedup of ∼30× with 88% of the memory usage while sacrificing 0.25 mag in depth compared to image-based shift-and-stack. These speedups enable the broad application of shift-and-stack to large-scale imaging surveys and searches for faint inner solar system objects. We provide a reference implementation via the find-asteroids Python package and this URL: https://github.com/stevenstetzler/find-asteroids .

The Astronomical JournalVol. 170(6)
Brigham Young University (US), Planetary Science Institute (US), Johns Hopkins University (US), University of California, Los Angeles (US), Lehigh University (US), Northern Arizona University (US), University of Michigan (US), Johns Hopkins University Applied Physics Laboratory (US), Stuttgart Technical University of Applied Sciences (DE), Large Synoptic Survey Telescope Corporation (US), Institute of Astronomy (RU), Center for Astrophysics Harvard & Smithsonian (US), University of Chile (CL), Carnegie Mellon University (US)
National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, Washington Research Foundation, University of Pennsylvania, Ohio State University, University of Washington, University of Portsmouth, University College London, University of Sussex, Deutsche Forschungsgemeinschaft, Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Financiadora de Estudos e Projetos, Institut de Física d'Altes Energies, University of Illinois at Urbana-Champaign, Institute for Data Intensive Research in Astrophysics and Cosmology, University of Washington, Science and Technology Facilities Council, Integrated Electronics Engineering Center, Binghamton University, Argonne National Laboratory, Fermilab, Lawrence Berkeley National Laboratory, SLAC National Accelerator Laboratory
Openalex Percentile: Top 75%
Astro and Planetary Science
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