de Sitter swampland conjecture in string field inflation
Bibliographic record
Abstract
Abstract In this paper, we study a particular type of inflation by using non-local Friedman equations that are derived from the zero levels of string field theory and express a tachyonic action. Then, we challenge it by further refining de Sitter (dS) swampland conjecture (FRdSSC) monitoring. Therefore, we investigate some quantities, such as potential and Hubble parameters. We also consider slow-roll parameters to examine quantities such as the scalar spectrum index and the tensor-to-scalar ratio. Using straightforward calculations, we investigate this model from the swampland conjecture perspective in terms of the cosmological parameters, i.e., ( $$n_s$$ n s ), (r), and observable data such as Planck 2018, by constructing some structures such as $$(c_{1,2}-n_s)$$ ( c 1 , 2 - n s ) and $$(c_{1,2}-r_s)$$ ( c 1 , 2 - r s ) . Then, we make a new restriction for this conjecture as $$c_1^2c_2^2$$ c 1 2 c 2 2 and get a limit for this model in the range $$c<0.0942$$ c < 0.0942 . We find this inflationary model is strongly in tension with the dS swampland conjecture (dSSC), i.e., $$c_1=c_2 \ne {\mathcal {O}}(1)$$ c 1 = c 2 ≠ O ( 1 ) . So, we shall challenge it with the FRdSSC, which has some free parameters, viz., $$a,b>0$$ a , b > 0 , $$a+b=1$$ a + b = 1 , and $$q>2$$ q > 2 . By setting these parameters, we examine the compatibility of the mentioned conjecture with this inflationary model. Finally, we infer from this string field inflation (SFI) model that it satisfies the FRdSSC with the constraint of its free parameters a, b, and q.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".