The DEEP2 Galaxy Redshift Survey: Clustering of Galaxies in Early Data
Bibliographic record
Abstract
We measure the two-point correlation function ξ(rp, π) in a sample of 2219 galaxies between z = 0.7 and 1.35 to a magnitude limit of RAB = 24.1 from the first season of the DEEP2 Galaxy Redshift Survey. From ξ(rp, 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/r0)-γ, on scales ~0.1-10 h-1 Mpc. In a sample with an effective redshift of zeff = 0.82, for a ΛCDM cosmology we find r0 = 3.53 ± 0.81 h-1 Mpc (comoving) and γ = 1.66 ± 0.12, while in a higher redshift sample with zeff = 1.14 we find r0 = 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, r0, than blue, emission-line, actively star-forming galaxies. Intrinsically brighter galaxies also cluster more strongly than fainter galaxies at z sime 1. Our results imply that the DEEP2 galaxies have an effective bias b = 0.96 ± 0.13 if σ8DM = 1 today or b = 1.19 ± 0.16 if σ8DM = 0.8 today. This bias is lower than that predicted by semianalytic simulations at z sime 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 sime 1 has selection criteria very similar to the Lyman break galaxies at z sime 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 sime 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 sime 1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".