A halo-based intrinsic-alignment model for simulation-based inference

Abstract

Simulation-based inference and other field-level weak-lensing analyses require intrinsic-alignment (IA) models generating realistic intrinsic-ellipticity fields across two-point and non-Gaussian observables. We develop a halo-based IA prescription with separate central and satellite components and test it against intrinsic galaxy shapes in FLAMINGO. We jointly fit two-point and higher-order statistics involving the IA, lensing, and density fields, including correlations between the intrinsic shapes of distinct galaxies, and compare against field-level nonlinear-alignment and density-weighted NLA prescriptions. We first evaluate the models at the positions of the original FLAMINGO galaxies, fixing the galaxy--matter connection and isolating the IA response. NLA and $\delta$NLA leave substantial discrepancies on nonlinear scales and across higher-order observables, whereas the halo model provides a simultaneous description of all statistics. We then remove the FLAMINGO galaxy catalogue and regenerate the source population from the halo field using a low-dimensional halo occupation model; the agreement is preserved even when the detailed occupation is only approximately reproduced. Much of this performance can be retained with a compact four-parameter portable model, and the same architecture transfers successfully to an independent gravity-only N-body realization with different halo definitions and shape measurements. These results show that nonlinear field-level IA modelling can be substantially improved over simple NLA-like prescriptions without introducing a large nuisance-parameter space. The halo model therefore provides a promising route for incorporating intrinsic alignments into simulation-based weak-lensing inference, although further validation across halo masses, hydrodynamical simulations, and realistic survey selections will be needed before application to data.