LyMAS reloaded: improving the predictions of the large-scale Lyman-<i>α</i> forest statistics from dark matter density and velocity fields
Sébastien Peirani, S. Prunet, S. Colombi, Christophe Pichon, David H. Weinberg, C. Laigle, Guilhem Lavaux, Yohan Dubois, Julien Devriendt
Abstract
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.