How fast does the WallGo? A package for computing wall velocities in first-order phase transitions

TL;DR

WallGo is an open-source Python package that accurately computes bubble wall velocities in first-order cosmological phase transitions, including out-of-equilibrium effects, improving precision over previous methods.

Contribution

It introduces the first public software to include out-of-equilibrium contributions in wall velocity calculations, utilizing spectral methods for exponential convergence.

Findings

Supports multiple out-of-equilibrium particles

Provides exponential convergence in collision term calculations

Tailored for non-runaway wall scenarios

Abstract

WallGo is an open source software for the computation of the bubble wall velocity in first-order cosmological phase transitions. It also computes the energy budget available for the generation of gravitational waves. The main part of WallGo, built in Python, determines the wall velocity by solving the scalar-field(s) equation of motion, the Boltzmann equations and energy-momentum conservation for the fluid velocity and temperature. WallGo also includes two auxiliary modules: WallGoMatrix, which computes matrix elements for out-of-equilibrium particles, and WallGoCollision, which performs higher-dimensional integrals for Boltzmann collision terms. Users can implement custom models by defining an effective potential and specifying a list of out-of-equilibrium particles and their interactions. As the first public software to compute the wall velocity including out-of-equilibrium contributions, WallGo improves the precision of the computation compared to common assumptions in earlier computations. It utilises a spectral method for the deviation from equilibrium and collision terms that provides exponential convergence in basis polynomials, and supports multiple out-of-equilibrium particles, allowing for Boltzmann mixing terms. WallGo is tailored for non-runaway wall scenarios where leading-order coupling effects dominate friction. While this work introduces the software and the underlying theory, a more detailed documentation can be found in https://wallgo.readthedocs.io.