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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 HYbrid Model Advected from LAgrangian space for IA (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 model and the tidal-alignment-tidal-torquing model. [ABSTRACT FROM AUTHOR]

We present an emulator for the two-point clustering of biased tracers in real space. We construct this emulator using neural networks calibrated with more than 400 cosmological models in a 8D cosmological parameter space that includes massive neutrinos an dynamical dark energy. The properties of biased tracers are described via a Lagrangian perturbative bias expansion which is advected to Eulerian space using the displacement field of numerical simulations. The cosmology-dependence is captured thanks to a cosmology-rescaling algorithm. We show that our emulator is capable of describing the power spectrum of galaxy formation simulations for a sample mimicking that of a typical Emission-Line survey at z ∼ 1 with an accuracy of |$1-2~{{\ \rm per\ cent}}$| up to non-linear scales |$k\sim 0.7 h\, {\rm Mpc}^{-1}$|⁠. [ABSTRACT FROM AUTHOR]

We study the relations among the parameters of the hybrid Lagrangian bias expansion model, fitting biased auto and cross power spectra up to |$k_{\rm max} = 0.7 \, h \, \mathrm{Mpc}^{-1}$|⁠. We consider ∼8000 halo and galaxy samples, with different halo masses, redshifts, galaxy number densities, and varying the parameters of the galaxy formation model. Galaxy samples are obtained through state-of-the-art extended subhalo abundance matching techniques and include both stellar mass and star formation rate selected galaxies. All of these synthetic galaxy samples are publicly available. We find that the hybrid Lagrangian bias model provides accurate fits to all of our halo and galaxy samples. The coevolution relations between galaxy bias parameters, although roughly compatible with those obtained for haloes, show systematic shifts and larger scatter. We explore possible sources of this difference in terms of dependence on halo occupation and assembly bias of each sample. The bias parameter relations displayed in this work can be used as a prior for future Bayesian analyses employing the hybrid Lagrangian bias expansion model. [ABSTRACT FROM AUTHOR]