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 hydrodyna...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2022
|
| _version_ | 1826308199619231744 |
|---|---|
| author | Peirani, S Prunet, S Colombi, S Pichon, C Weinberg, DH Laigle, C Lavaux, G Dubois, Y Devriendt, J |
| author_facet | Peirani, S Prunet, S Colombi, S Pichon, C Weinberg, DH Laigle, C Lavaux, G Dubois, Y Devriendt, J |
| author_sort | Peirani, S |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T07:15:57Z |
| format | Journal article |
| id | oxford-uuid:d935a280-eeac-48a3-8e60-e8b75cc0f56c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:15:57Z |
| publishDate | 2022 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:d935a280-eeac-48a3-8e60-e8b75cc0f56c2022-07-29T09:54:41ZLyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fieldsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:d935a280-eeac-48a3-8e60-e8b75cc0f56cEnglishSymplectic ElementsOxford University Press2022Peirani, SPrunet, SColombi, SPichon, CWeinberg, DHLaigle, CLavaux, GDubois, YDevriendt, JWe 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. |
| spellingShingle | Peirani, S Prunet, S Colombi, S Pichon, C Weinberg, DH Laigle, C Lavaux, G Dubois, Y Devriendt, J LyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fields |
| title | LyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fields |
| title_full | LyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fields |
| title_fullStr | LyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fields |
| title_full_unstemmed | LyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fields |
| title_short | LyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fields |
| title_sort | lymas reloaded improving the predictions of the large scale lyman α forest statistics from dark matter density and velocity fields |
| work_keys_str_mv | AT peiranis lymasreloadedimprovingthepredictionsofthelargescalelymanaforeststatisticsfromdarkmatterdensityandvelocityfields AT prunets lymasreloadedimprovingthepredictionsofthelargescalelymanaforeststatisticsfromdarkmatterdensityandvelocityfields AT colombis lymasreloadedimprovingthepredictionsofthelargescalelymanaforeststatisticsfromdarkmatterdensityandvelocityfields AT pichonc lymasreloadedimprovingthepredictionsofthelargescalelymanaforeststatisticsfromdarkmatterdensityandvelocityfields AT weinbergdh lymasreloadedimprovingthepredictionsofthelargescalelymanaforeststatisticsfromdarkmatterdensityandvelocityfields AT laiglec lymasreloadedimprovingthepredictionsofthelargescalelymanaforeststatisticsfromdarkmatterdensityandvelocityfields AT lavauxg lymasreloadedimprovingthepredictionsofthelargescalelymanaforeststatisticsfromdarkmatterdensityandvelocityfields AT duboisy lymasreloadedimprovingthepredictionsofthelargescalelymanaforeststatisticsfromdarkmatterdensityandvelocityfields AT devriendtj lymasreloadedimprovingthepredictionsofthelargescalelymanaforeststatisticsfromdarkmatterdensityandvelocityfields |