2017
DOI: 10.1088/1475-7516/2017/05/053
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Inflation in random Gaussian landscapes

Abstract: We develop analytic and numerical techniques for studying the statistics of slow-roll inflation in random Gaussian landscapes. As an illustration of these techniques, we analyze small-field inflation in a one-dimensional landscape. We calculate the probability distributions for the maximal number of e-folds and for the spectral index of density fluctuations n s and its running α s . These distributions have a universal form, insensitive to the correlation function of the Gaussian ensemble. We outline possible … Show more

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Cited by 29 publications
(78 citation statements)
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“…Generally we expect that long inflation requires fine-tuning, so P prior (N e ) is a decreasing function of N e . For a random Gaussian landscape one finds [17,18] P prior (N e ) ∝ N −3 e . (4.3)…”
Section: Probability Distribution For Spatial Curvaturementioning
confidence: 99%
“…Generally we expect that long inflation requires fine-tuning, so P prior (N e ) is a decreasing function of N e . For a random Gaussian landscape one finds [17,18] P prior (N e ) ∝ N −3 e . (4.3)…”
Section: Probability Distribution For Spatial Curvaturementioning
confidence: 99%
“…[6] for a review and refs. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] for recent works.) 2 Thermal inflation is another possible solution to the cosmological moduli problem, where the moduli density is diluted by a mini-inflation around the TeV scale [23][24][25][26].…”
Section: Jhep01(2018)053mentioning
confidence: 99%
“…Following the argument in section 4, this is expected whenever one of the isocurvature modes' masses becomes tachyonic. [35] predict a RGF inflating at saddle points has an approximate powerlaw e-fold distribution P (N e ) ∝ N γ e , with γ = −3. Here, we plot our empirical e-fold probability from all datasets.…”
Section: Aggregate Datamentioning
confidence: 96%
“…It is unclear, however, to what extent, if any, the potentials generated through this method are RGFs. At a given point, a true RGF, described by (1.2), has correlations between all even-ordered Taylor coefficients, and all odd-ordered Taylor coefficients while the odd and even coefficients are uncorrelated [35,36,44]. The DBM approach only constraints the Hessian coefficients.…”
Section: Introductionmentioning
confidence: 99%