Bibliographic record
Abstract
A bstract Early Dark Energy (EDE) is a prominent model to resolve the Hubble tension, which employs a dynamical axion with a periodic potential. In this work, we take first steps towards the embedding of this model into stable compactifications of string theory. First, we provide a pedagogical review of the EDE scenario and its main challenges. Second, we construct a simple supergravity toy model using only minimal ingredients. Already at this level, we can understand the origin of the harmonics of the EDE scalar potential in terms of a delicate balance of the leading terms from separate non-perturbative effects. Third and final, we embed the model into a KKLT-type compactification, with the EDE scalar field realized by a two-form axion. We find that a successful embedding, with all moduli stabilized, requires restrictive assumptions, which are lacking at present a known string theory implementation mechanism, both on the Pfaffians and on the exponents of the non-perturbative terms responsible for the EDE dynamics. We point out that such non-generic conditions reflect well known challenges of the EDE model and further investigation might guide us towards a conclusive resolution. An alternative viewpoint suggests that rather than serving as the foundation for a string theory embedding, these conditions might instead indicate that EDE is, in fact, incompatible with string theory, at least in the realizations we study here. Further investigation will be needed to ascertain the status of EDE in string theory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".