Speaker
Description
Once LISA provides data, the resulting constraints on, or detection of, a stochastic gravitational wave background will be analyzed in the context of various sources, particularly cosmological phase transitions. This analysis will not only cover the general characteristics of these phase transitions but also explore the underlying particle physics models that generate them. While the LISA collaboration can directly interpret the general phase transition signals, the detailed interpretation in terms of specific particle physics models — given the wide range of possible models — will require extensive input from the broader scientific community.
This project aims to develop a computational tool that streamlines the reinterpretation of gravitational wave (GW) signals from cosmological phase transitions in terms of parameters of underlying particle physics models. The software will take GW signal parameters as reconstructed from simulated data — expressed either in terms of geometric parameters (characteristic frequencies, slopes, and amplitude) or thermodynamic parameters (transition strength, bubble size, temperature, and wall velocity) — and map them into the parameter space of specific particle physics models.
Users will input a parameter scan, mapping model parameters (such as couplings and masses) to the corresponding GW signal parameters. The tool will then generate parameter fits, including 68% and 95% confidence level contours, within the model’s parameter space, based on provided datasets (e.g., simulated injected signals, Monte Carlo chains, or likelihood fits). This approach builds on the framework outlined in Section 6 of arXiv:2403.03723, with improvements to automate and enhance the process.
In addition, a database of benchmark models and associated GW signals will be established. This will leverage existing parameter scans (such as those from the Phase Transition Parameter Estimation Project, arXiv:2403.03723) and incorporate new models and scans as needed.