Evolution of the luminosity-to-halo mass relation of LRGs from a combined analysis of SDSS-DR10+RCS2
Bibliographic record
Abstract
We study the evolution of the luminosity-to-halo mass relation of luminous red galaxies (LRGs). We selected a sample of 52 000 LOWZ and CMASS LRGs from the Baryon Oscillation Spectroscopic Survey (BOSS) SDSS-DR10 in the ~450 deg2 that overlaps with imaging data from the second Red-sequence Cluster Survey (RCS2), grouped them into bins of absolute magnitude and redshift and measured their weak-lensing signals. The source redshift distribution has a median of 0.7, which allowed us to study the lensing signal as a function of lens redshift. We interpreted the lensing signal using a halo model, from which we obtained the halo masses as well as the normalisations of the mass-concentration relations. The concentration of haloes that host LRGs is consistent with dark-matter-only simulations once we allow for miscentering or satellites in the modelling. The slope of the luminosity-to-halo mass relation has a typical value of 1.4 and does not change with redshift, but we find evidence for a change in amplitude: the average halo mass of LOWZ galaxies increases by 25-14+16% between z = 0.36 and 0.22 to an average value of (6.43 ± 0.52) × 1013 h70-1M⊙. If we extend the redshift range using the CMASS galaxies and assume that they are the progenitors of the LOWZ sample, the average mass of LRGs increases by 80+39-28\% between z = 0.6 and 0.2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.001 |
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".