Non-Gaussian and nonscale-invariant perturbations from tachyonic preheating in hybrid inflation
Bibliographic record
Abstract
We show that in hybrid inflation it is possible to generate large second-order perturbations in the cosmic microwave background due to the instability of the tachyonic field during preheating. We carefully calculate this effect from the tachyon contribution to the gauge-invariant curvature perturbation, clarifying some confusion in the literature concerning nonlocal terms in the tachyon curvature perturbation; we show explicitly that such terms are absent. We quantitatively compute the non-Gaussianity generated by the tachyon field during the preheating phase and translate the experimental constraints on the nonlinearity parameter ${f}_{NL}$ into constraints on the parameters of the model. We also show that nonscale-invariant second-order perturbations from the tachyon field with spectral index $n=4$ can become larger than the inflaton-generated first-order perturbations, leading to stronger constraints than those coming from non-Gaussianity. The width of the excluded region in terms of the logarithm of the dimensionless coupling $g$, grows linearly with the log of the ratio of the Planck mass to the tachyon VEV, $\mathrm{log}({M}_{p}/v)$; hence very large regions are ruled out if the inflationary scale $v$ is small. We apply these results to string-theoretic brane-antibrane inflation, and find a stringent upper bound on the string coupling, ${g}_{s}<{10}^{\ensuremath{-}4.5}$.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".