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arXiv · 2307.13754

HYMALAIA: A Hybrid Lagrangian Model for Intrinsic Alignments

Francisco Maion·Raul E. Angulo·Thomas Bakx·Nora Elisa Chisari·Toshiki Kurita·Marcos Pellejero-Ibáñez
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Abstract

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.

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Francisco Maion, Raul E. Angulo, Thomas Bakx, Nora Elisa Chisari, Toshiki Kurita, Marcos Pellejero-Ibáñez. 2023-07-25. HYMALAIA: A Hybrid Lagrangian Model for Intrinsic Alignments. https://doi.org/10.1093/mnras%2Fstae1331

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