LyMAS: Predicting Large-Scale Lyman-alpha Forest Statistics from the
  Dark Matter Density Field
  [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.
