LyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fields. Issue 3 (17th May 2022) - Peirani, S; Prunet, S; Colombi, S; Pichon, C; Weinberg, D H; Laigle, C; Lavaux, G; Dubois, Y; Devriendt, J
ABSTRACT: 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 DM 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 DM smoothing (0.3, 0.5, and 1.0 Mpc  h −1 comoving). We have tried different combinations of DM fields and found that LyMAS2, applied to the Horizon-noAGN DM 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 two-point correlation functions of pseudo-spectra generated with LyMAS2 can be recovered with relative differences of ∼5 per cent even for high angles, the flux 1D power spectrum (along the light of sight) with ∼2 per cent and the flux 1D probability distribution function exactly. Finally, we have produced several large mock BOSS spectra (1.0 and 1.5 Gpc  h −1 ) expected to lead to much more reliable and accurate theoretical predictions.