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The halo model as a versatile tool to predict intrinsic alignments

Maria Cristina FortunaLeiden Observatory, Leiden University, PO Box 9513, Leiden, NL-2300 RA, the NetherlandsHenk HoekstraLeiden Observatory, Leiden University, PO Box 9513, Leiden, NL-2300 RA, the NetherlandsB. JoachimiDepartment of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UKHarry JohnstonDepartment of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UKNora Elisa ChisariInstitute for Theoretical Physics, Utrecht University, Princetonplein 5, NL-3584 CE Utrecht, the NetherlandsChristos GeorgiouLeiden Observatory, Leiden University, PO Box 9513, Leiden, NL-2300 RA, the NetherlandsConstance MahonyDepartment of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK
2020en
ABI

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ABSTRACT Intrinsic alignments (IAs) of galaxies are an important contaminant for cosmic shear studies, but the modelling is complicated by the dependence of the signal on the source galaxy sample. In this paper, we use the halo model formalism to capture this diversity and examine its implications for Stage-III and Stage-IV cosmic shear surveys. We account for the different IA signatures at large and small scales, as well as for the different contributions from central/satellite and red/blue galaxies, and we use realistic mocks to account for the characteristics of the galaxy populations as a function of redshift. We inform our model using the most recent observational findings: we include a luminosity dependence at both large and small scales and a radial dependence of the signal within the halo. We predict the impact of the total IA signal on the lensing angular power spectra, including the current uncertainties from the IA best-fits to illustrate the range of possible impact on the lensing signal: the lack of constraints for fainter galaxies is the main source of uncertainty for our predictions of the IA signal. We investigate how well effective models with limited degrees of freedom can account for the complexity of the IA signal. Although these lead to negligible biases for Stage-III surveys, we find that, for Stage-IV surveys, it is essential to at least include an additional parameter to capture the redshift dependence.

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