LyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fields

We present LyMAS2, an improved version of the "Lyman-α Mass Association Scheme" aiming at predicting the large-scale 3d clustering statistics of the Lyman-α forest (Ly-α) from moderate resolution simulations of the dark matter (DM) distribution, with prior calibrations from high resolution hydrodynamical simulations of smaller volumes. In this study, calibrations are derived from the Horizon-AGN suite simulations, (100 Mpc/h)^3 comoving volume, using Wiener filtering, combining information from dark matter density and velocity fields (i.e. velocity dispersion, vorticity, line of sight 1d-divergence and 3d-divergence). All new predictions have been done at z=2.5 in redshift-space, while considering the spectral resolution of the SDSS-III BOSS Survey and different dark matter smoothing (0.3, 0.5 and 1.0 Mpc/h comoving). We have tried different combinations of dark matter fields and found that LyMAS2, applied to the Horizon-noAGN dark matter fields, significantly improves the predictions of the Ly-α 3d clustering statistics, especially when the DM overdensity is associated with the velocity dispersion or the vorticity fields. Compared to the hydrodynamical simulation trends, the 2-point correlation functions of pseudo-spectra generated with LyMAS2 can be recovered with relative differences of ~5% even for high angles, the flux 1d power spectrum (along the light of sight) with ~2% and the flux 1d probability distribution function exactly. Finally, we have produced several large mock BOSS spectra (1.0 and 1.5 Gpc/h) expected to lead to much more reliable and accurate theoretical predictions.