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