Context. The Milne-Eddington approximation provides an analytic and simple solution to the radiative transfer equation. It can be easily implemented in inversion codes used to fit spectro-polarimetric observations and infer average values of the magnetic field vector and the line-of-sight velocity of the solar plasma. However, in principle, it is

Stokes inversion techniques are very powerful methods for obtaining information on the thermodynamic and magnetic properties of solar and stellar atmospheres. In recent years, highly sophisticated inversion codes have been developed that are now routinely applied to spectro-polarimetric observations. Most of these inversion codes are designed to

Context. In modern astronomy, machine learning has proved to be efficient and effective in mining big data from the newest telescopes. Aims: In this study, we construct a supervised machine-learning algorithm to classify the objects in the Javalambre Photometric Local Universe Survey first data release (J-PLUS DR1). Methods: The sample set is

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