Halo-model signatures from 380 000 Sloan Digital Sky Survey luminous red galaxies with photometric redshifts
Bibliographic record
Abstract
We analyse the small-scale clustering in ‘MegaZ-LRG’, a large photometric redshift catalogue of luminous red galaxies extracted from the imaging data set of the Sloan Digital Sky Survey. MegaZ-LRG, presented in a companion paper, spans the redshift range 0.4 < z < 0.7 with an rms redshift error σz≈ 0.03(1 +z), covering 5914 deg2 to map out a total cosmic volume 2.5 h−3 Gpc3. In this study we use 380 000 photometric redshifts to measure significant deviations from the canonical power-law fit to the angular correlation function in a series of narrow redshift slices, in which we construct volume-limited samples. These deviations are direct signatures of the manner in which these galaxies populate the underlying network of dark matter haloes. We cleanly delineate the separate contributions of the ‘one-halo’ and ‘two-halo’ clustering terms and fit our measurements by parametrizing the halo occupation distribution N(M) of the galaxies. Our results are successfully fitted by a ‘central’ galaxy contribution with a ‘soft’ transition from zero to one galaxy, combined with a power-law ‘satellite’ galaxy component, the slope of which is a strong function of galaxy luminosity. The large majority of galaxies are classified as central objects of their host dark matter haloes rather than satellites in more massive systems. The effective halo mass of MegaZ-LRG galaxies lies in the range log10(Meff/h−1M⊙) = 13.61–13.80 (increasing with redshift assuming large-scale normalization σ8= 0.8) for corresponding number densities in the range ng= 5.03 − 0.56 × 104h−3 Mpc−3. Our results confirm the usefulness of the halo model for gaining physical insight into the patterns of galaxy clustering.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".