Cosmological implications of baryon acoustic oscillation measurements
Bibliographic record
Abstract
We derive constraints on cosmological parameters and tests of dark energy models from the combination of baryon acoustic oscillation (BAO) measurements with cosmic microwave background (CMB) data and a recent reanalysis of Type Ia supernova (SN) data. In particular, we take advantage of high-precision BAO measurements from galaxy clustering and the Lyman-$\ensuremath{\alpha}$ forest (LyaF) in the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS). Treating the BAO scale as an uncalibrated standard ruler, BAO data alone yield a high confidence detection of dark energy; in combination with the CMB angular acoustic scale they further imply a nearly flat universe. Adding the CMB-calibrated physical scale of the sound horizon, the combination of BAO and SN data into an ``inverse distance ladder'' yields a measurement of ${H}_{0}=67.3\ifmmode\pm\else\textpm\fi{}1.1\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$, with 1.7% precision. This measurement assumes standard prerecombination physics but is insensitive to assumptions about dark energy or space curvature, so agreement with CMB-based estimates that assume a flat $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ cosmology is an important corroboration of this minimal cosmological model. For constant dark energy ($\mathrm{\ensuremath{\Lambda}}$), our $\mathrm{BAO}+\mathrm{SN}+\mathrm{CMB}$ combination yields matter density ${\mathrm{\ensuremath{\Omega}}}_{m}=0.301\ifmmode\pm\else\textpm\fi{}0.008$ and curvature ${\mathrm{\ensuremath{\Omega}}}_{k}=\ensuremath{-}0.003\ifmmode\pm\else\textpm\fi{}0.003$. When we allow more general forms of evolving dark energy, the $\mathrm{BAO}+\mathrm{SN}+\mathrm{CMB}$ parameter constraints are always consistent with flat $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ values at $\ensuremath{\approx}1\ensuremath{\sigma}$. While the overall ${\ensuremath{\chi}}^{2}$ of model fits is satisfactory, the LyaF BAO measurements are in moderate ($2--2.5\ensuremath{\sigma}$) tension with model predictions. Models with early dark energy that tracks the dominant energy component at high redshift remain consistent with our expansion history constraints, and they yield a higher ${H}_{0}$ and lower matter clustering amplitude, improving agreement with some low redshift observations. Expansion history alone yields an upper limit on the summed mass of neutrino species, $\ensuremath{\sum}{m}_{\ensuremath{\nu}}<0.56\text{ }\text{ }\mathrm{eV}$ (95% confidence), improving to $\ensuremath{\sum}{m}_{\ensuremath{\nu}}<0.25\text{ }\text{ }\mathrm{eV}$ if we include the lensing signal in the Planck CMB power spectrum. In a flat $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model that allows extra relativistic species, our data combination yields ${N}_{\mathrm{eff}}=3.43\ifmmode\pm\else\textpm\fi{}0.26$; while the LyaF BAO data prefer higher ${N}_{\mathrm{eff}}$ when excluding galaxy BAO, the galaxy BAO alone favor ${N}_{\mathrm{eff}}\ensuremath{\approx}3$. When structure growth is extrapolated forward from the CMB to low redshift, standard dark energy models constrained by our data predict a level of matter clustering that is high compared to most, but not all, observational estimates.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".