Accelerated inference on accelerated cosmic expansion: New constraints on axionlike early dark energy with DESI BAO and ACT DR6 CMB lensing
Bibliographic record
Abstract
The early dark energy (EDE) extension to Λ cold dark matter ( Λ CDM ) has been proposed as a candidate scenario to resolve the “Hubble tension.” We present new constraints on the EDE model by incorporating new data from the Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillation (BAO) survey and cosmic microwave background (CMB) lensing measurements from the Atacama Cosmology Telescope (ACT) sixth data release and Planck NPIPE data. We do not find evidence for EDE. The maximum fractional contribution of EDE to the total energy density is f EDE < 0.091 [95% confidence level (CL)] from our baseline combination of Planck CMB, CMB lensing, and DESI BAO. Our strongest constraints on EDE come from the combination of Planck CMB and CMB lensing alone, yielding f EDE < 0.070 ( 95 % CL ) . We also explore extensions of Λ CDM beyond the EDE parameters by treating the total neutrino mass as a free parameter, finding ∑ m ν < 0.096 eV ( 95 % CL ) and f EDE < 0.087 ( 95 % CL ) . For the first time in EDE analyses, we perform Bayesian parameter estimation using neural network emulators of cosmological observables, which are on the order of 100 times faster than full Boltzmann solutions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".