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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 10–10³× 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 thefind-asteroidsPython package and this URL:https://github.com/stevenstetzler/find-asteroids. 

Abstract We present a detailed study of the observational biases of the DECam Ecliptic Exploration Project’s B1 data release and survey simulation software that enables direct statistical comparisons between models and our data. We inject a synthetic population of objects into the images, and then subsequently recover them in the same processing as our real detections. This enables us to characterize the survey’s completeness as a function of apparent magnitudes and on-sky rates of motion. We study the statistically optimal functional form for the magnitude, and develop a methodology that can estimate the magnitude and rate efficiencies for all survey’s pointing groups simultaneously. We have determined that our peak completeness is on average 80% in each pointing group, and our magnitude drops to 25% of this value atm₂₅= 26.22. We describe the freely available survey simulation software and its methodology. We conclude by using it to infer that our effective search area for objects at 40 au is 14.8 deg², and that our lack of dynamically cold distant objects means that there at most 8 × 10³objects with 60 <a< 80 au and absolute magnitudesH≤ 8. 

Abstract We present the first set of trans-Neptunian objects (TNOs) observed on multiple nights in data taken from the DECam Ecliptic Exploration Project. Of these 110 TNOs, 105 do not coincide with previously known TNOs and appear to be new discoveries. Each individual detection for our objects resulted from a digital tracking search at TNO rates of motion, using two-to-four-hour exposure sets, and the detections were subsequently linked across multiple observing seasons. This procedure allows us to find objects with magnitudesm_VR≈ 26. The object discovery processing also included a comprehensive population of objects injected into the images, with a recovery and linking rate of at least 94%. The final orbits were obtained using a specialized orbit-fitting procedure that accounts for the positional errors derived from the digital tracking procedure. Our results include robust orbits and magnitudes for classical TNOs with absolute magnitudesH∼ 10, as well as a dynamically detached object found at 76 au (semimajor axisa≈ 77 au). We find a disagreement between our population of classical TNOs and the CFEPS-L7 three-component model for the Kuiper Belt. 

Abstract Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counterintuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfvén waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold,α= 2 as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed >600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: preflare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine thatα= 1.63 ± 0.03. This is below the critical threshold, suggesting that Alfvén waves are an important driver of coronal heating.