High Energy Physics - Theory
[Submitted on 11 Oct 2018 (v1), last revised 21 Dec 2018 (this version, v2)]
Title:Learning to Inflate
View PDFAbstract:Motivated by machine learning, we introduce a novel method for randomly generating inflationary potentials. Namely, we treat the Taylor coefficients of the potential as weights in a single-layer neural network and use gradient ascent to maximize the number of e-folds of inflation. Inflationary potentials "learned" in this way develop a critical point, which is typically a local maximum but may also be an inflection point. We study the phenomenology of the models along the gradient ascent trajectory, finding substantial agreement with experiment for large-field local maximum models and small-field inflection point models. For two-field models of inflation, the potential eventually learns a genuine multi-field model in which the inflaton curves significantly during the course of its descent.
Submission history
From: Tom Rudelius [view email][v1] Thu, 11 Oct 2018 17:59:15 UTC (7,735 KB)
[v2] Fri, 21 Dec 2018 14:49:53 UTC (7,736 KB)
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