Wide Field Imaging of the Hubble Deep Field South Region II: The Evolution of Galaxy Clustering at z < 1 1
Bibliographic record
Abstract
We present the galaxy-galaxy angular correlations as a function of photometric redshift in a deep-wide galaxy survey centered on the Hubble Deep Field South. Images were obtained with the Big Throughput Camera on the Blanco 4m telescope at CTIO, of 1/2 square degree in broad-band uBV RI, reaching ∼ 24 mag. Approximately 40,000 galaxies are detected in the survey. We determine photometric redshifts using galaxy template fitting to the photometry. Monte Carlo simulations show that redshifts from these data should be reliable out to z ∼ 1, where the 4000 ˚A break shifts into the I band. The inferred redshift distribution, n(z), shows good agreement with the distribution of galaxies measured in the HDF North and the Canada-France Redshift Survey. After assigning galaxies to redshift bins with width ∆z = 0.33, we determine the two point angular correlation function in each bin. We find that the amplitude of the correlation, Aw, drops across the three bins to redshift z ∼ 1. Simple ǫ models of clustering evolution fit this result, with the best agreement for ǫ = 0. Hierarchical cold-dark-matter models best fit in a low density, Λ-dominated universe. Subject headings: clustering cosmology: observations — galaxies: evolution – galaxies: 1 Based on observations obtained at Cerro Tololo Inter-American Observatory, a division of the National
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.001 | 0.000 |
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".