Examining the Evidence for Dynamical Dark Energy
Bibliographic record
Abstract
We apply a new nonparametric Bayesian method for reconstructing the evolution history of the equation of state w of dark energy, based on applying a correlated prior for w(z), to a collection of cosmological data. We combine the latest supernova (SNLS 3 year or Union 2.1), cosmic microwave background, redshift space distortion, and the baryonic acoustic oscillation measurements (including BOSS, WiggleZ, and 6dF) and find that the cosmological constant appears consistent with current data, but that a dynamical dark energy model which evolves from w<-1 at z~0.25 to w>-1 at higher redshift is mildly favored. Estimates of the Bayesian evidence show little preference between the cosmological constant model and the dynamical model for a range of correlated prior choices. Looking towards future data, we find that the best fit models for current data could be well distinguished from the ΛCDM model by observations such as Planck and Euclid-like surveys.
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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 | 0.003 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".