Is the trans-Planckian censorship a swampland conjecture?
Bibliographic record
Abstract
During an accelerated expansion of the Universe, quantum fluctuations of sub-Planckian size can be stretched outside the horizon and be regarded effectively classical. Recently, it has been conjectured that such horizon-crossing of trans-Planckian modes never happens inside theories of quantum gravity (the trans-Planckian censorship conjecture, TCC). We point out several conceptual problems of this conjecture, which is in itself formulated as a statement on the restriction of possible scenarios in a theory: by contrast a standard swampland conjecture is a restriction of possible theories in the landscape of the quantum gravity. We emphasize the concept of swampland universality, i.e., that a swampland conjecture constrains any possible scenario in a given effective field theory. In order to illustrate the problems clearly we introduce several versions of the conjecture, where the TCC condition is imposed differently to scenarios realizable in a given theory. We point out that these different versions of the conjecture lead to observable differences: a TCC violation in another Universe can exclude a theory, and such reduction of the landscape restricts possible predictions in our Universe. Our analysis raises the question of whether or not the trans-Planckian censorship conjecture can be regarded as a swampland conjecture concerning the existence of UV completion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".