LyMAS: Predicting Large-Scale Lyman-alpha Forest Statistics from the Dark Matter Density Field
Abstract
[abridged] We describe LyMAS (Ly-alpha Mass Association Scheme), a method of predicting clustering statistics in the Ly-alpha forest on large scales from moderate resolution simulations of the dark matter distribution, with calibration from high-resolution hydrodynamic simulations of smaller volumes. We use the "Horizon MareNostrum" simulation, a 50 Mpc/h comoving volume evolved with the adaptive mesh hydrodynamic code RAMSES, to compute the conditional probability distribution P(F_s|delta_s) of the transmitted flux F_s, smoothed (1-dimensionally) over the spectral resolution scale, on the dark matter density contrast delta_s, smoothed (3-dimensionally) over a similar scale. In this study we adopt the spectral resolution of the SDSS-III BOSS at z=2.5, and we find optimal results for a dark matter smoothing length sigma=0.3 Mpc/h (comoving). In extended form, LyMAS exactly reproduces both the 1-dimensional power spectrum and 1-point flux distribution of the hydro simulation spectra. Applied to the MareNostrum dark matter field, LyMAS accurately predicts the 2-point conditional flux distribution and flux correlation function of the full hydro simulation for transverse sightline separations as small as 1 Mpc/h, including redshift-space distortion effects. It is substantially more accurate than a deterministic density-flux mapping ("Fluctuating Gunn-Peterson Approximation"), often used for large volume simulations of the forest. With the MareNostrum calibration, we apply LyMAS to 1024^3 N-body simulations of a 300 Mpc/h and 1.0 Gpc/h cube to produce large, publicly available catalogs of mock BOSS spectra that probe a large comoving volume. LyMAS will be a powerful tool for interpreting 3-d Ly-alpha forest data, thereby transforming measurements from BOSS and other massive quasar absorption surveys into constraints on dark energy, dark matter, space geometry, and IGM physics.
Cite
@article{arxiv.1306.1533,
title = {LyMAS: Predicting Large-Scale Lyman-alpha Forest Statistics from the Dark Matter Density Field},
author = {Sébastien Peirani and David H. Weinberg and Stéphane Colombi and Jérémy Blaizot and Yohan Dubois and Christophe Pichon},
journal= {arXiv preprint arXiv:1306.1533},
year = {2014}
}
Comments
Accepted for publication in ApJ (minor corrections from the previous version). Catalogs of mock BOSS spectra and relevant data can be found at: http://www2.iap.fr/users/peirani/lymas/lymas.htm