Dynamic Observing and Tiling Strategies for the DESI Legacy Surveys
Bibliographic record
Abstract
Abstract The Dark Energy Spectroscopic Instrument Legacy Surveys, a combination of three ground-based imaging surveys, have mapped 16,000 deg2 in three optical bands (g, r, and z) to a depth 1–2 mag deeper than the Sloan Digital Sky Survey. Our work addresses one of the major challenges of wide-field imaging surveys conducted at ground-based observatories: the varying depth that results from varying observing conditions at Earth-bound sites. To mitigate these effects, the Legacy Surveys (the Dark Energy Camera Legacy Survey, or DECaLS; the Mayall z-band Legacy Survey, or MzLS; and the Beiijing-Arizona Sky Survey, or BASS) employed a unique strategy to dynamically adjust the exposure times as rapidly as possible in response to the changing observing conditions. We present the tiling and observing strategies used by the first two of these surveys. We demonstrate that the tiling and dynamic observing strategies jointly result in a more uniform-depth survey that has higher efficiency for a given total observing time compared with the traditional approach of using fixed exposure times.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".