A Large Massive Quiescent Galaxy Sample at z ∼ 1.2
Bibliographic record
Abstract
Abstract In this paper we present a simple color–magnitude selection and obtain a large sample of 33,893 massive quiescent galaxies at intermediate redshifts (1 < z < 1.5). We choose the longest wavelength available in the Hyper-Supreme-Cam (HSC) deep survey, the Y band and i − Y color, to select the 4000 Å Balmer jump in passive galaxies to the highest redshift possible within the survey. With the rich multiwavelength data in the HSC deep fields, we then confirm that the selected galaxies are in the targeted redshift range of 1 < z < 1.5, lie in the passive region of the UVJ diagram, and have high stellar masses at log(M */M ⊙) > 10.5, with a median of log(M */M ⊙) = 11.0. A small fraction of our galaxies is also covered by the HST CANDELS. Morphological analysis in the observed H band shows that the majority of this subsample are early-type galaxies. As massive early-type galaxies trace the high-density regions in the large-scale structure in the universe, our study provides a quick and simple way to obtain a statistically significant sample of massive galaxies in a relatively narrow redshift range. Our sample is 7–20× larger at the massive end (log(M */M ⊙) > 10.5) than any existing samples obtained in previous surveys. This is a pioneer study, and the technique introduced here can be applied to a future wide-field survey to study large-scale structure and to identify high-density regions and clusters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".