Speaker
Description
We present DeepSSM, an open-source code powered by neural networks (NNs)
to emulate gravitational wave (GW) spectra produced by sound waves during cosmological
first-order phase transitions in the radiation-dominated era. The training data is obtained
from an enhanced version of the Sound Shell Model (SSM), which accounts for the effects
of cosmic expansion and yields more accurate spectra in the infrared regime. The emulator
enables instantaneous predictions of GW spectra given the phase transition parameters, while
achieving agreement with the enhanced SSM model within 10% accuracy in the worst case
scenarios. The emulator is highly computationally efficient and fully differentiable, making
it particularly suitable for direct Bayesian inference on phase transition parameters without
relying on empirical templates, such as broken power-law models. We demonstrate this capability by successfully reconstructing phase transition parameters and their degeneracies from
mock LISA observations using a Hamiltonian Monte Carlo sampler.
