The DEEP2 Galaxy Redshift Survey: Clustering of Galaxies in Early Data
Bibliographic record
Abstract
We measure the two-point correlation function ξ( r p , π) in a sample of 2219 galaxies between z = 0.7 and 1.35 to a magnitude limit of R AB = 24.1 from the first season of the DEEP2 Galaxy Redshift Survey. From ξ( r p , π) we recover the real-space correlation function, ξ( r ), which we find can be approximated within the errors by a power law, ξ( r ) = ( r / r 0 ) -γ , on scales ~0.1-10 h -1 Mpc. In a sample with an effective redshift of z eff = 0.82, for a ΛCDM cosmology we find r 0 = 3.53 ± 0.81 h -1 Mpc (comoving) and γ = 1.66 ± 0.12, while in a higher redshift sample with z eff = 1.14 we find r 0 = 3.12 ± 0.72 h -1 Mpc and γ = 1.66 ± 0.12. These errors are estimated from mock galaxy catalogs and are dominated by the cosmic variance present in the current data sample. We find that red, absorption-dominated, passively evolving galaxies have a larger clustering scale length, r 0 , than blue, emission-line, actively star-forming galaxies. Intrinsically brighter galaxies also cluster more strongly than fainter galaxies at z ≃ 1. Our results imply that the DEEP2 galaxies have an effective bias b = 0.96 ± 0.13 if σ 8DM = 1 today or b = 1.19 ± 0.16 if σ 8DM = 0.8 today. This bias is lower than that predicted by semianalytic simulations at z ≃ 1, which may be the result of our R -band target selection. We discuss possible evolutionary effects within our survey volume, and we compare our results with galaxy-clustering studies at other redshifts, noting that our star-forming sample at z ≃ 1 has selection criteria very similar to the Lyman break galaxies at z ≃ 3 and that our red, absorption-line sample displays a clustering strength comparable to the expected clustering of the Lyman break galaxy descendants at z ≃ 1. Our results demonstrate that galaxy-clustering properties as a function of color, spectral type, and luminosity seen in the local universe were largely in place by z ≃ 1.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".