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Intrinsic Alignments in IllustrisTNG and their implications for weak lensing: Tidal shearing and tidal torquing mechanisms put to the test

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arxiv 2010.07951 v1 pith:H6REK5KK submitted 2020-10-15 astro-ph.GA astro-ph.CO

classification astro-ph.GAastro-ph.CO
keywords galaxiestidalsignalintrinsicspiralalignmentlinearmodel
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Accurate measurements of the cosmic shear signal require a separation of the true weak gravitational lensing signal from intrinsic shape correlations of galaxies. These `intrinsic alignments' of galaxies originate from galaxy formation processes and are expected to be correlated with the gravitational field through tidal processes affecting the galaxies, such as tidal shearing for elliptical galaxies and tidal torquing for spiral galaxies. In this study, we use morphologically selected samples of elliptical and spiral galaxies from the IllustrisTNG simulation at z=0 and z=1 to test the commonly employed linear (tidal shearing) and quadratic (tidal torquing) models for intrinsic alignments. We obtain local measurements of the linear and quadratic alignment parameters, including corrections for large-scale anisotropies of the cosmologically small simulation volume, and study their dependence on galaxy and environmental properties. We find a significant alignment signal for elliptical galaxies (linear model), that increases with mass and redshift. Spiral galaxies (quadratic model) on the other hand exhibit a significant signal only for the most massive objects at z=1. We show the quadratic model for spiral galaxies to break down at its fundamental assumptions, and simultaneously obtain a significant signal of spiral galaxies to align according to the linear model. We use the derived alignment parameters to compute intrinsic alignment spectra and estimate the expected contamination in the weak lensing signal obtained by Euclid.

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Cited by 1 Pith paper

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  1. Learning Intrinsic Alignments from Local Galaxy Environments

    astro-ph.CO 2025-06 conditional novelty 7.0 of 10

    DELTA uses an equivariant graph neural network with a probabilistic orientation output to recover injected galaxy intrinsic alignments from mock catalogs dominated by noise.

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