Spitzer Matching Survey of the UltraVISTA Ultra-deep Stripes (SMUVS): Full-mission IRAC Mosaics and Catalogs
Bibliographic record
Abstract
Abstract This paper describes new deep 3.6 and 4.5 μm imaging of three UltraVISTA near-infrared survey stripes within the COSMOS field. The observations were carried out with Spitzer’s Infrared Array Camera (IRAC) for the Spitzer Matching Survey of the UltraVISTA Deep Stripes (SMUVS). In this work we present our data reduction techniques, and document the resulting mosaics, coverage maps, and catalogs in both IRAC passbands for the three easternmost UltraVISTA survey stripes, covering a combined area of about 0.66 deg2, of which 0.45 deg2 have at least 20 hr of integration time. SMUVS reaches point-source sensitivities of about 25.0 AB mag (0.13 μJy) at both 3.6 and 4.5 μm, with a significance of 4σ, accounting for both survey sensitivity and source confusion. To this limit the SMUVS catalogs contain a total of ∼350,000 sources, each of which is detected significantly in at least one IRAC band. Because of its uniform and high sensitivity, relatively large area coverage, and the wide array of ancillary data available in COSMOS, the SMUVS survey will be useful for a large number of cosmological investigations. We make all images and catalogs described herein publicly available via the Spitzer Science Center.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".