%0 Generic %1 peirani2022lymas %A Peirani, S. %A Prunet, S. %A Colombi, S. %A Pichon, C. %A Weinberg, D. H. %A Laigle, C. %A Lavaux, G. %A Dubois, Y. %A Devriendt, J. %D 2022 %K library %T LyMAS reloaded: improving the predictions of the large-scale Lyman-\alpha forest statistics from dark matter density and velocity fields %U http://arxiv.org/abs/2204.06365 %X We present LyMAS2, an improved version of the "Lyman-\alpha Mass Association Scheme" aiming at predicting the large-scale 3d clustering statistics of the Lyman-\alpha forest (Ly-\alpha) 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-\alpha 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.

@misc{peirani2022lymas, abstract = {We present LyMAS2, an improved version of the "Lyman-{\alpha} Mass Association Scheme" aiming at predicting the large-scale 3d clustering statistics of the Lyman-{\alpha} forest (Ly-{\alpha}) 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-{\alpha} 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.}, added-at = {2022-04-14T10:23:29.000+0200}, author = {Peirani, S. and Prunet, S. and Colombi, S. and Pichon, C. and Weinberg, D. H. and Laigle, C. and Lavaux, G. and Dubois, Y. and Devriendt, J.}, biburl = {https://www.bibsonomy.org/bibtex/2cef2e941dd0e14c20d7600b9d8e7910a/gpkulkarni}, description = {LyMAS reloaded: improving the predictions of the large-scale Lyman-{\alpha} forest statistics from dark matter density and velocity fields}, interhash = {5eab4827cf892805efd0892257ee95fe}, intrahash = {cef2e941dd0e14c20d7600b9d8e7910a}, keywords = {library}, note = {cite arxiv:2204.06365Comment: Submitted to MNRAS (one iteration with the referee) - 29 pages}, timestamp = {2022-04-14T10:23:29.000+0200}, title = {LyMAS reloaded: improving the predictions of the large-scale Lyman-{\alpha} forest statistics from dark matter density and velocity fields}, url = {http://arxiv.org/abs/2204.06365}, year = 2022 }