Diagnosing DASH: A Catalog of Structural Properties for the COSMOS-DASH Survey
Bibliographic record
Abstract
Abstract We present the H 160 morphological catalogs for the COSMOS-DASH survey, the largest area near-IR survey using HST-WFC3 to date. Utilizing the “Drift And SHift” observing technique for HST-WFC3 imaging, the COSMOS-DASH survey imaged approximately 0.5 deg2 of the UltraVISTA deep stripes (0.7 deg2, when combined with archival data). Global structural parameters are measured for 51,586 galaxies within COSMOS-DASH using GALFIT (excluding the CANDELS area) with detection using a deep multi-band HST image. We recover consistent results with those from the deeper 3D-HST morphological catalogs, finding that, in general, sizes and Sérsic indices of typical galaxies are accurate to limiting magnitudes of H 160 < 23 and H 160 < 22 ABmag, respectively. In size-mass parameter space, galaxies in COSMOS-DASH demonstrate robust morphological measurements out to z ∼ 2 and down to log ( M ⋆ / M ⊙ ) ∼ 9 . With the advantage of the larger area of COSMOS-DASH, we measure a flattening of the quiescent size-mass relation below log ( M ⋆ / M ⊙ ) ∼ 10.5 that persists out to z ∼ 2. We show that environment is not the primary driver of this flattening, at least out to z = 1.2, whereas internal physical processes may instead govern the structural evolution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".