Exploring Two-Field Inflation in the Wess-Zumino Model

We explore inflation via the effective potential of the minimal Wess-Zumino model, considering both the real and imaginary components of the complex field. Using transport techniques, we calculate the full allowed range of $n_s$, $r$ and $f_{\rm NL}$ for different choices of the single free parameter, $v$, and present the probability distribution of these signatures given a simple choice for the prior distribution of initial conditions. Our work provides a case study of multi-field inflation in a simple but realistic setting, with important lessons that are likely to apply more generally. For example, we find that there are initial conditions consistent with observations of $n_s$ and $r$ for values of $v$ that would be excluded if only evolutions in the real field direction were to be considered, and that these may yield enhanced values of $f_{\rm NL}$. Moreover, we find that initial conditions fixed at high energy density, where the potential is close to quartic in form, can still lead to evolutions in a concave region of the potential during the observable number of e-folds, as preferred by present data. The Wess-Zumino model therefore provides an illustration that multi-field dynamics must be taken into account when seeking to understand fully the phenomenology of such models of inflation.Comment: 19 pages, 6 figure

PyTransport: A Python package for the calculation of inflationary correlation functions

21 pages, 5 figures21 pages, 5 figures21 pages, 5 figuresPyTransport constitutes a straightforward code written in C++ together with Python scripts which automatically edit, compile and run the C++ code as a Python module. It has been written for Unix-like systems (OS X and Linux). Primarily the module employs the transport approach to inflationary cosmology to calculate the tree-level power-spectrum and bispectrum of user specified models of multi-field inflation, accounting for all sub and super-horizon effects. The transport method we utilise means only coupled differential equations need to be solved, and the implementation presented here combines the speed of C++ with the functionality and convenience of Python. At present the code is restricted to canonical models. This document details the code and illustrates how to use it with a worked example