Planck constraints on holographic dark energy
Bibliographic record
Abstract
We perform a detailed investigation on the cosmological constraints on the holographic dark energy (HDE) model by using the Planck data. HDE can provide a good fit to Planck high-l (l>40) temperature power spectrum, while the discrepancy at l=20-40 found in LCDM remains unsolved in HDE. The Planck data alone can lead to strong and reliable constraint on the HDE parameter c. At 68% CL, we get c=0.508+-0.207 with Planck+WP+lensing, favoring the present phantom HDE at > 2sigma CL. Comparably, by using WMAP9 alone we cannot get interesting constraint on c. By combining Planck+WP with the BAO measurements from 6dFGS+SDSS DR7(R)+BOSS DR9, the H0 measurement from HST, the SNLS3 and Union2.1 SNIa data sets, we get 68% CL constraints c=0.484+-0.070, 0.474+-0.049, 0.594+-0.051 and 0.642+-0.066. Constraints can be improved by 2%-15% if we further add the Planck lensing data. Compared with the WMAP9 results, the Planck results reduce the error by 30%-60%, and prefer a phantom-like HDE at higher CL. We find no evident tension between Planck and BAO/HST. Especially, the strong correlation between Omegam h^3 and dark energy parameters is helpful in relieving the tension between Planck and HST. The residual chi^2_{Planck+WP+HST}-chi^2_{Planck+WP} is 7.8 in LCDM, and is reduced to 1.0 or 0.3 if we switch dark energy to the w model or the holographic model. We find SNLS3 is in tension with all other data sets; for Planck+WP, WMAP9 and BAO+HST, the corresponding Delta chi^2 is 6.4, 3.5 and 4.1, respectively. Comparably, Union2.1 is consistent with these data sets, but the combination Union2.1+BAO+HST is in tension with Planck+WP+lensing, corresponding to a Delta chi^2 8.6 (1.4% probability). Thus, it is not reasonable to perform an all-combined (CMB+SNIa+BAO+HST) analysis for HDE when using the Planck data. Our tightest self-consistent constraint is c=0.495+-0.039 obtained from Planck+WP+BAO+HST+lensing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".