Constraining early dark energy with large-scale structure
Bibliographic record
Abstract
An axion-like field comprising $\ensuremath{\sim}10%$ of the energy density of the Universe near matter-radiation equality is a candidate to resolve the Hubble tension; this is the ``early dark energy'' (EDE) model. However, as shown in Hill et al., the model fails to simultaneously resolve the Hubble tension and maintain a good fit to both cosmic microwave background (CMB) and large-scale structure (LSS) data. Here, we use redshift-space galaxy clustering data to sharpen constraints on the EDE model. We perform the first EDE analysis using the full-shape power spectrum likelihood from the Baryon Oscillation Spectroscopic Survey (BOSS), based on the effective field theory (EFT) of LSS. The inclusion of this likelihood in the EDE analysis yields a 25% tighter error bar on ${H}_{0}$ compared to primary CMB data alone, yielding ${H}_{0}=68.5{4}_{\ensuremath{-}0.95}^{+0.52}\text{ }\text{ }\mathrm{km}/\mathrm{s}/\mathrm{Mpc}$ (68% C.L.). In addition, we constrain the maximum fractional energy density contribution of the EDE to ${f}_{\mathrm{EDE}}<0.072$ (95% C.L.). We explicitly demonstrate that the EFT BOSS likelihood yields much stronger constraints on EDE than the standard BOSS likelihood. Including further information from photometric LSS surveys,the constraints narrow by an additional 20%, yielding ${H}_{0}=68.7{3}_{\ensuremath{-}0.69}^{+0.42}\text{ }\text{ }\mathrm{km}/\mathrm{s}/\mathrm{Mpc}$ (68% C.L.) and ${f}_{\mathrm{EDE}}<0.053$ (95% C.L.). These bounds are obtained without including local-Universe ${H}_{0}$ data, which is in strong tension with the CMB and LSS, even in the EDE model. We also refute claims that Markov-chain Monte Carlo analyses of EDE that omit SH0ES from the combined dataset yield misleading posteriors. Finally, we show that upcoming Euclid/DESI-like spectroscopic galaxy surveys will greatly improve the EDE constraints. We conclude that current data preclude the EDE model as a resolution of the Hubble tension, and that future LSS surveys can close the remaining parameter space of this model.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".