Bibliographic record
Abstract
Models of our universe lack consistency at different energy scales, so we require a theory with ultraviolet (UV) completion such as string theory. A suitable candidate to model our universe in this framework is de Sitter space, a spacetime which expands and has positive curvature. When describing the expansion of this space, however, one computes the wrong sign for the cosmological constant that would not allow for an expanding universe. This motivates one to consider corrections from a quantum theory to reproduce the correct positive sign for the cosmological constant. The conditions that cause this incorrect sign are known as the \textit{swampland criteria}, and prevent de Sitter space from being realized in a consistent manner at different energy scales. We look at a framework to avoid the swampland restriction in a UV-complete theory by considering de Sitter space resulting from compactifications of type IIB superstring theory. In particular, we demonstrate that the definitions of particles in an expanding UV-incomplete theory leads to inconsistencies in the definition of the de Sitter vacuum states. Furthermore, we review previous attempts to prevent these inconsistencies by constructing coherent states that expand and have the desired de Sitter isometries over supersymmetric Minkowski space. These states add quantum corrections to the metric operator, resulting in the cosmological constant carrying the correct sign. Therefore, the de Sitter space can be used in a UV-complete theory to model our universe.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".