Stochastic analysis of finite-temperature effects on cosmological parameters by artificial neural networks
This paper employs artificial neural networks and stochastic optimization to analyze finite-temperature quantum gravity effects on cosmological parameters, demonstrating that incorporating new temperature-dependent density terms improves the fit to Planck data and suggests a non-negligible role for thermal quantum corrections in cosmological evolution.