Quantifying the effect of cooled initial conditions on cosmic string network evolution

TL;DR

This paper investigates how cooled initial conditions affect the evolution of cosmic string networks, using GPU-accelerated simulations and an improved calibration pipeline for the velocity-dependent one-scale model, revealing minimal impact of small cooling and the robustness of the calibration method.

Contribution

It introduces an improved MCMC calibration pipeline for the VOS model and assesses the impact of cooling on cosmic string network evolution, demonstrating robustness and minimal effects of small cooling.

Findings

Small cooling has no significant impact on model calibration.

Longer dissipation periods noticeably affect the network evolution.

The new calibration pipeline is robust and scalable for larger simulations.

Abstract

Quantitative studies of the evolution and cosmological consequences of networks of cosmic strings (or other topological defects) require a combination of numerical simulations and analytic modeling with the velocity-dependent one-scale (VOS) model. In previous work, we demonstrated that a GPU-accelerated code for local Abelian-Higgs string networks enables a statistical separation of key dynamical processes affecting the evolution of the string networks and thus a precise calibration of the VOS model. Here we further exploit this code in a detailed study of two important aspects connecting the simulations with the VOS model. First, we study the sensitivity of the model calibration to the presence (or absence) of thermal oscillations due to high gradients in the initial conditions. This is relevant since in some Abelian-Higgs simulations described in the literature a period of artificial (unphysical) dissipation---usually known as cooling---is introduced with the goal of suppressing these oscillations and accelerating the convergence to scaling. We show that a small amount of cooling has no statistically significant impact on the VOS model calibration, while a longer dissipation period does have a noticeable effect. Second, in doing this analysis we also introduce an improved Markov Chain Monte Carlo based pipeline for calibrating the VOS model, Comparison to our previous bootstrap based pipeline shows that the latter accurately determined the best-fit values of the VOS model parameter, but underestimated the uncertainties in some of the parameters. Overall, our analysis shows that the calibration pipeline is robust and can be applied to future much larger field theory simulations.