Modeling Lyman-\alpha\ Forest Cross-Correlations with LyMAS
  We use the Ly-$\alpha$ Mass Association Scheme (LyMAS; Peirani et al. 2014)
to predict cross-correlations at $z=2.5$ between dark matter halos and
transmitted flux in the Ly-$\alpha$ forest, and compare to cross-correlations
measured for quasars and damped Ly-$\alpha$ systems (DLAs) from the Baryon
Oscillation Spectroscopic Survey (BOSS) by Font-Ribera et al. (2012, 2013). We
calibrate LyMAS using Horizon-AGN hydrodynamical cosmological simulations of a
$(100\ h^{-1}\ \mathrm{Mpc})^3$ comoving volume. We apply this calibration to a
$(1\ h^{-1}\ \mathrm{Gpc})^3$ simulation realized with $2048^3$ dark matter
particles. In the 100 $h^{-1}$ Mpc box, LyMAS reproduces the halo-flux
correlations computed from the full hydrodynamic gas distribution very well. In
the 1 $h^{-1}$ Gpc box, the amplitude of the large scale cross-correlation
tracks the halo bias $b_h$ as expected. We provide empirical fitting functions
that describe our numerical results. In the transverse separation bins used for
the BOSS analyses, LyMAS cross-correlation predictions follow linear theory
accurately down to small scales. Fitting the BOSS measurements requires
inclusion of random velocity errors; we find best-fit RMS velocity errors of
399 km s$^{-1}$ and 252 km s$^{-1}$ for quasars and DLAs, respectively. We
infer bias-weighted mean halo masses of $M_h/10^{12}\
h^{-1}M_\odot=2.19^{+0.16}_{-0.15}$ and $0.69^{+0.16}_{-0.14}$ for the host
halos of quasars and DLAs, with $\sim 0.2$ dex systematic uncertainty
associated with redshift evolution, IGM parameters, and selection of data
fitting range.
