Simulation-based inference from the Lyman-alpha forest 1D power spectrum with CAMELS
The study used AI (a normalizing flow neural network) to infer cosmological and astrophysical parameters from Lyman-alpha forest data by learning directly from simulations. By training on two different galaxy formation models, the method successfully overcame inconsistencies between models, providing unbiased constraints on cosmology even when using a combined training approach.