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

Journal article

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.

Authors: Peirani, S; Prunet, S; Colombi, S; Pichon, C; Weinberg, DH

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Oxford University Press
• Journal: Monthly Notices of the Royal Astronomical Society
• Volume: 514
• Issue: 3
• Pages: 3222-3245
• Publication date: 2022-05-17
• Acceptance date: 2022-05-07
• DOI: 10.1093/mnras/stac1344
• EISSN: 1365-2966
• ISSN: 0035-8711
• Language: English
• Keywords: FFR; dark matter; methods: numerical