HYMALAIA: A Hybrid Lagrangian Model for Intrinsic Alignments

The intrinsic alignment of galaxies is an important ingredient for modelling weak-lensing measurements, and a potentially valuable cosmological and astrophysical signal. In this paper, we present HYMALAIA: a new model to predict the intrinsic alignments of biased tracers. HYMALAIA is based on a perturbative expansion of the statistics of the Lagrangian shapes of objects, which is then advected to Eulerian space using the fully non-linear displacement field obtained from $N$-body simulations. We demonstrate that HYMALAIA is capable of consistently describing monopole and quadrupole of halo shape-shape and matter-shape correlators, and that, without increasing the number of free parameters, it does so more accurately than other perturbatively inspired models such as the non-linear alignment (NLA) model and the tidal-alignment-tidal-torquing (TATT) model.