The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: towards a computationally efficient analysis without informative priors
Bibliographic record
Abstract
We develop a new methodology called double-probe analysis with the aim of minimizing informative priors in the estimation of cosmological parameters. We extract the dark-energy-model-independent cosmological constraints from the joint data sets of Baryon Oscillation Spectroscopic Survey (BOSS) galaxy sample and Planck cosmic microwave background (CMB) measurement. We measure the mean values and covariance matrix of $\{R$, $l_a$, $Ω_b h^2$, $n_s$, $log(A_s)$, $Ω_k$, $H(z)$, $D_A(z)$, $f(z)σ_8(z)\}$, which give an efficient summary of Planck data and 2-point statistics from BOSS galaxy sample, where $R=\sqrt{Ω_m H_0^2}\,r(z_*)$, and $l_a=πr(z_*)/r_s(z_*)$, $z_*$ is the redshift at the last scattering surface, and $r(z_*)$ and $r_s(z_*)$ denote our comoving distance to $z_*$ and sound horizon at $z_*$ respectively. The advantage of this method is that we do not need to put informative priors on the cosmological parameters that galaxy clustering is not able to constrain well, i.e. $Ω_b h^2$ and $n_s$. Using our double-probe results, we obtain $Ω_m=0.304\pm0.009$, $H_0=68.2\pm0.7$, and $σ_8=0.806\pm0.014$ assuming $Λ$CDM; and $Ω_k=0.002\pm0.003$ and $w=-1.00\pm0.07$ assuming o$w$CDM. The results show no tension with the flat $Λ$CDM cosmological paradigm. By comparing with the full-likelihood analyses with fixed dark energy models, we demonstrate that the double-probe method provides robust cosmological parameter constraints which can be conveniently used to study dark energy models. We extend our study to measure the sum of neutrino mass and obtain $Σm_ν<0.10/0.22$ (68\%/95\%) assuming $Λ$CDM and $Σm_ν<0.26/0.52$ (68\%/95\%) assuming $w$CDM. This paper is part of a set that analyses the final galaxy clustering dataset from BOSS.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".