Stochastic analysis of finite-temperature effects on cosmological parameters by artificial neural networks
This work investigates how finite-temperature quantum gravity effects change cosmological parameters like the Hubble parameter by adding temperature-dependent corrections to existing codes. Using machine learning, researchers constrained these new parameters against Planck data, suggesting these quantum gravity effects are important for refining cosmological models.