Speaker
Description
Cosmological first-order phase transitions can generate stochastic gravitational wave backgrounds observable by LISA, offering a probe of physics beyond the Standard Model. Extracting phase transition parameters from LISA observations requires comparing observations to theoretical predictions across a high-dimensional parameter space. However, running a full lattice simulation for each parameter point is prohibitively expensive.
We address this challenge using the Sound Shell Model, a computationally efficient framework that reproduces the results of lattice simulations for intermediate-strength transitions. We have implemented the Sound Shell Model in the Python-based simulation library PTtools, enabling systematic large-scale exploration of the parameter space. Our implementation incorporates key physical effects, including variations in the sound speed, thermal suppression of bubble nucleation, and the finite lifetime of the acoustic source.
In addition, we extend the web-based tool PTPlot to support double broken power law (DBPL) fits and Sound Shell Model spectra, providing more accurate alternatives to the standard broken power law (BPL) approximations. These developments provide a fast and flexible framework for modeling stochastic gravitational wave signals from phase transitions, enabling likelihood-based parameter inference with LISA.