Preheating and reheating effects of the Kähler moduli inflation I model
Bibliographic record
Abstract
We investigate reheating in the string-theory-motivated K\"ahler moduli inflation I potential, coupled to a light scalar field $\ensuremath{\chi}$ and produce constraints and forecasts based on cosmic microwave background and gravitational wave observables. We implement a Markov chain Monte Carlo sampling method to compute the adopted model's parameter ranges allowed by the current cosmic microwave background observations. Floquet analysis and numerical lattice simulations are performed to analyze the nonlinear effects of the model's (p)reheating phase. We derive bounds on the $\mathrm{\ensuremath{\Lambda}}$-cold dark matter parameters ${A}_{s}$, ${n}_{s}$, ${n}_{\mathrm{run}}$, and $r$ based on Planck results, finding that correlations between model parameters severely constrain the range of these parameters allowed within this model. While the K\"ahler moduli inflation I potential's nonvanishing minimum may provide a possible source for the observed dark energy density ${\ensuremath{\rho}}_{\mathrm{DE}}$ this cannot be tested with current observations. We estimate the 95% CI bounds on the inflaton mass ${m}_{\ensuremath{\phi}}$ and reheating temperature ${T}_{\mathrm{reh}}$ to be $2.1\ifmmode\times\else\texttimes\fi{}{10}^{13}\text{ }\text{ }\mathrm{GeV}\ensuremath{\lesssim}{m}_{\ensuremath{\phi}}\ensuremath{\lesssim}3.2\ifmmode\times\else\texttimes\fi{}{10}^{13}\text{ }\text{ }\mathrm{GeV}$ and ${T}_{\mathrm{reh}}\ensuremath{\gtrsim}1.8\ifmmode\times\else\texttimes\fi{}{10}^{3}\text{ }\text{ }\mathrm{GeV}$, respectively. We observe both self-resonance and parametric resonance instability band structures in our Floquet analysis results. Finally, we do not observe any formation of oscillon configurations in our lattice simulations; however, our results predict a stochastic gravitational wave background generated during preheating that would be observable today in the ${10}^{9}--{10}^{11}\text{ }\text{ }\mathrm{Hz}$ frequency range.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".