The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: mock galaxy catalogues for the BOSS Final Data Release
Bibliographic record
Abstract
We reproduce the galaxy clustering catalogue from the SDSS-III Baryon Oscillation Spectroscopic Survey Final Data Release (BOSS DR11&DR12) with high fidelity on all relevant scales in order to allow a robust analysis of baryon acoustic oscillations and redshift space distortions. We have generated (6000) 12 288 MultiDark patchy BOSS (DR11) DR12 light cones corresponding to an effective volume of ∼192 000 [h−1 Gpc]3 (the largest ever simulated volume), including cosmic evolution in the redshift range from 0.15 to 0.75. The mocks have been calibrated using a reference galaxy catalogue based on the halo abundance matching modelling of the BOSS DR11&DR12 galaxy clustering data and on the data themselves. The production follows three steps. First, we apply the patchy code to generate a dark matter field and an object distribution including non-linear stochastic galaxy bias. Secondly, we run the halo/stellar distribution reconstruction hadron code to assign masses to the various objects. This step uses the mass distribution as a function of local density and non-local indicators (i.e. tidal field tensor eigenvalues and relative halo exclusion separation for massive objects) from the reference simulation applied to the corresponding patchy dark matter and galaxy distribution. Finally, we apply the sugar code to build the light cones. The resulting MultiDarkpatchy mock light cones reproduce the number density, selection function, survey geometry, and in general within 1σ, for arbitrary stellar mass bins, the power spectrum up to k = 0.3 h Mpc−1, the two-point correlation functions down to a few Mpc scales, and the three-point statistics of the BOSS DR11&DR12 galaxy samples.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".