This work presents a new physically motivated supervised machine-learning method, HYDRO-BAM, to reproduce the three-dimensional Lyα forest field in real and redshift space, which learns from a reference hydrodynamic simulation and thereby saves about seven orders of magnitude in computing time. We show that our method is accurate up to k ~ 1 h Mpc

The evolution of rotation and magnetic activity of solar-like stars is key to improve our knowledge of stars (e.g. ages). Several spectroscopic surveys have aimed to characterize the magnetic activity of solar-like stars in order to put the Sun into context and in time. By investigating the magnetic activity of other stars in a different parameter