3D-DASH: The Widest Near-infrared Hubble Space Telescope Survey
Bibliographic record
Abstract
Abstract The 3D-Drift And SHift (3D-DASH) program is a Hubble Space Telescope (HST) WFC3 F160W imaging and G141 grism survey of the equatorial COSMOS field. 3D-DASH extends the legacy of HST near-infrared imaging and spectroscopy to degree-scale swaths of the sky, enabling the identification and study of distant galaxies (z > 2) that are rare or in short-lived phases of galaxy evolution at rest-frame optical wavelengths. Furthermore, when combined with existing ACS/F814W imaging, the program facilitates spatially resolved studies of the stellar populations and dust content of intermediate redshift (0.5 < z < 2) galaxies. Here we present the reduced F160W imaging mosaic available to the community. Observed with the efficient DASH technique, the mosaic comprises 1256 individual WFC3 pointings, corresponding to an area of 1.35 deg2 (1.43 deg2 in 1912 when including archival data). The median 5σ point-source limit in H 160 is 24.74 ± 0.20 mag. We also provide a point-spread function (PSF) generator tool to determine the PSF at any location within the 3D-DASH footprint. 3D-DASH is the widest HST/WFC3 imaging survey in the F160W filter to date, increasing the existing extragalactic survey area in the near-infrared at HST resolution by an order of magnitude.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".