Measuring the cosmic evolution of dark energy with baryonic oscillations in the galaxy power spectrum
Bibliographic record
Abstract
We use Monte Carlo techniques to simulate the ability of future large high-redshift galaxy surveys to measure the temporal evolution of the dark energy equation-of-state w(z), using the baryonic acoustic oscillations in the clustering power spectrum as a 'standard ruler'. Our analysis only utilizes the oscillatory component of the power spectrum and not its overall shape, which is potentially susceptible to broadband tilts induced by a host of model-dependent systematic effects. Our results are therefore robust and conservative. We show that baryon oscillation constraints can be thought of, to high accuracy, as a direct probe of the distance - redshift and expansion rate - redshift relations where distances are measured in units of the sound horizon. Distance precisions of 1% are obtainable for a fiducial redshift survey covering 10,000 deg^2 and redshift range 0.5 < z < 3.5. If the dark energy is further characterized by w(z) = w_0 + w_1 z (with a cut-off in the evolving term at z = 2), we can then measure the parameters w_0 and w_1 with a precision exceeding current knowledge by a factor of ten: 1 sigma accuracies Delta w_0 ~ 0.03 and Delta w_1 ~ 0.06 are obtainable (assuming a flat universe and that the other cosmological parameters Omega_m} and h could be measured independently to a precision of +/- 0.01 by combinations of future CMB and other experiments). We quantify how this performance degrades with redshift/areal coverage and knowledge of Omega_m and h, and discuss realistic observational prospects for such large-scale spectroscopic redshift surveys, with a variety of diverse techniques. We also quantify how large photometric redshift imaging surveys could be utilized to produce measurements of (w_0,w_1) with the baryonic oscillation method which may be competitive in the short term.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".