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Robust Inflation from fibrous strings

机译:纤维绳的强劲充气

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Successful inflationary models should (i) describe the data well; (ii) arise generically from sensible UV completions; (iii) be insensitive to detailed fine-tunings of parameters and (iv) make interesting new predictions. We argue that a class of models with these properties is characterized by relatively simple potentials with a constant term and negative exponentials. We here continue earlier work exploring UV completions for these models including the key (though often ignored) issue of modulus stabilisation - to assess the robustness of their predictions. We show that string models where the inflaton is a fibration modulus seem to be robust due to an effective resealing symmetry, and fairly generic since most known Calabi-Yau manifolds are fibrations. This class of models is characterized by a generic relation between the tensor-to-scalar ratio r and the spectral index n(s) of the form r proportional to (n(s) - 1)(2) where the proportionality constant depends on the nature of the effects used to develop the inflationary potential and the topology of the internal space. In particular we find that the largest values of the tensor -to -scalar ratio that can be obtained by generalizing the original set-up are of order r less than or similar to 0.01. We contrast this general picture with specific popular models, such as the Starobinsky scenario and alpha-attractors. Finally, we argue the self consistency of large-field inflationary models can strongly constrain non-supersymmetric inflationary mechanisms.
机译:成功的通货膨胀模型应(i)很好地描述数据; (ii)一般是由于明智的紫外线完井而产生的; (iii)对详细的参数微调不敏感,以及(iv)做出有趣的新预测。我们认为,具有这些属性的一类模型的特征是具有相对常数的常数和负指数的简单电位。我们在这里继续进行较早的工作,探索这些模型的UV完整度,包括模数稳定的关键(尽管经常被忽略)问题-评估其预测的鲁棒性。我们显示,由于有效的重新密封对称性,在其中充胀气是纤维化模量的弦模型似乎很健壮,并且由于大多数已知的Calabi-Yau流形都是纤维化而相当通用。这类模型的特征在于张量与标量之比r与形式为r的光谱指数n(s)之间的一般关系与(n(s)-1)(2)成比例,比例常数取决于用于产生通货膨胀潜力的效应的性质以及内部空间的拓扑结构。特别地,我们发现可以通过推广原始设置而获得的张量与标量比的最大值的r阶小于或等于0.01。我们将这种总体情况与特定的流行模型(例如Starobinsky场景和alpha吸引子)进行对比。最后,我们认为大范围通胀模型的自洽性可以强烈限制非超对称通胀机制。

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