Small-scale clustering of BOSS galaxies: dependence on luminosity, colour, age, stellar mass, specific star formation rate, and other properties
Bibliographic record
Abstract
ABSTRACT We measure and analyse galaxy clustering and the dependence on luminosity, colour, age, stellar mass, and specific star formation rate using Baryon Oscillation Spectroscopic Survey (BOSS) galaxies at 0.48 < z < 0.62. We fit the monopole and quadrupole moments of the two-point correlation function and its projection on scales of 0.1–60.2 h−1 Mpc, after having split the catalogue in a variety of ways. We find that the clustering dependence is consistent with previous well-established results showing the broad trends expected: For example, that brighter, redder, older, more massive and quenched galaxies are more strongly clustered. We also investigate the dependence on additional parameters previously derived from stellar population synthesis model fits to the spectra. We find that galaxy clustering depends on look-back formation time at a low level, while it has little dependence on metallicity. To understand the physics behind these trends, we fit the clustering with a simulation-based emulator to simultaneously model cosmology and galaxy bias using a halo occupation distribution framework. After marginalizing parameters determining the background cosmology, galaxy bias, and a scaling parameter to decouple halo velocity field, we find that the growth rate of large-scale structure as determined by the redshift space distortions is consistent with previous analysis using the full sample, and we do not find evidence that cosmological constraints depend systematically on galaxy selection. This demonstrates that cosmological inference using small-scale clustering measurements is robust to changes in the catalogue selection.
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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 | 0.001 | 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) | 0.001 | 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".