We extend current models of the halo occupation distribution (HOD) to include
a flexible, empirical framework for the forward modeling of the intrinsic
alignment (IA) of galaxies. A primary goal of this work is to produce mock
galaxy catalogs for the purpose of validating existing models and methods for
the mitigation of IA in weak lensing measurements. This technique can also be
used to produce new, simulation-based predictions for IA and galaxy clustering.
Our model is probabilistically formulated, and rests upon the assumption that
the orientations of galaxies exhibit a correlation with their host dark matter
(sub)halo orientation or with their position within the halo. We examine the
necessary components and phenomenology of such a model by considering the
alignments between (sub)halos in a cosmological dark matter only simulation. We
then validate this model for a realistic galaxy population in a set of
simulations in the Illustris-TNG suite. We create an HOD mock with
Illustris-like correlations using our method, constraining the associated IA
model parameters, with the χdof2​ between our model's correlations
and those of Illustris matching as closely as 1.4 and 1.1 for
orientation--position and orientation--orientation correlation functions,
respectively. By modeling the misalignment between galaxies and their host
halo, we show that the 3-dimensional two-point position and orientation
correlation functions of simulated (sub)halos and galaxies can be accurately
reproduced from quasi-linear scales down to 0.1 h−1Mpc. We also find
evidence for environmental influence on IA within a halo. Our
publicly-available software provides a key component enabling efficient
determination of Bayesian posteriors on IA model parameters using observational
measurements of galaxy-orientation correlation functions in the highly
nonlinear regime.Comment: 17 pages, 12 figures, 3 tables, for submission to The Open Journal of
Astrophysics, code available at https://github.com/astropy/halotool