TransitionListener v2.0 -- Robust gravitational wave predictions for cosmological phase transitions

TL;DR

TransitionListener v2.0 is a Python framework that enables precise predictions of gravitational wave signals from cosmological phase transitions, incorporating improved physical modeling and numerical stability for various detector sensitivities.

Contribution

It introduces a self-consistent treatment of transition dynamics and a direct bubble separation calculation, enhancing accuracy over previous tools.

Findings

Improved physical consistency and numerical stability in GW predictions.

Effective modeling of strongly supercooled and ultraslow transitions.

Compatibility with multiple GW detectors and Bayesian inference.

Abstract

Gravitational wave backgrounds from strong first-order cosmological phase transitions are key observational targets predicted by many SM extensions and might be observed by current and future observatories like LISA, the Einstein Telescope or pulsar timing arrays (PTAs). Still, their precise forecast given a specific model remains a challenge. In this article, we present TransitionListener v2.0, a Python framework for precision studies of cosmological phase transitions and their associated gravitational wave (GW) signals. The code provides an end-to-end pipeline from a user-defined scalar potential to GW spectra and signal-to-noise ratios, enabling both benchmark studies and large-scale parameter scans. Version 2 introduces a self-consistent treatment of the transition dynamics, including the evolution of the true-vacuum fraction and its backreaction on the Hubble expansion, as well as a consistent description of reheating during percolation. A direct computation of the mean bubble separation allows to faithfully map to the GW spectral templates from bubble collisions, sound waves, and turbulence stemming from state-of-the-art simulations. TransitionListener includes built-in sensitivity curves for space- and ground-based detectors and PTAs, interfaces to PTA likelihoods, and wrappers for Bayesian model inference and high-dimensional parameter scans. Compared to existing public tools, TransitionListener v2.0 improves the physical consistency and numerical stability of GW predictions across a wide range of models, with particular emphasis on the strongly supercooled and ultraslow transition regime where conventional approximations break down and the most promising GW signals are expected.