2020
DOI: 10.1093/mnras/staa718
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Testing tidal alignment models for anisotropic correlations of halo ellipticities with N-body simulations

Abstract: There is a growing interest of using the intrinsic alignment (IA) of galaxy images as a tool to extract cosmological information complimentary to galaxy clustering analysis. Recently, Okumura & Taruya derived useful formulas for the intrinsic ellipticity-ellipticity correlation, the gravitational shear-intrinsic ellipticity correlation, and the velocity-intrinsic ellipticity correlation functions based on the linear alignment (LA) model. In this paper, using large-volume N -body simulations, we measure these a… Show more

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Cited by 24 publications
(21 citation statements)
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“…(16). These contain most of the information [21] and will therefore be the power spectra of interest. They are given by…”
Section: Fisher Forecast and Resultsmentioning
confidence: 99%
See 3 more Smart Citations
“…(16). These contain most of the information [21] and will therefore be the power spectra of interest. They are given by…”
Section: Fisher Forecast and Resultsmentioning
confidence: 99%
“…In this article, the aim is to show that measuring the correlation between shapes of galaxies and the underlying velocity field of the large scale structure is within the reach of upcoming galaxy surveys. To this end, we predict this correlation relying on the linear alignment model as described in [18,20,21]. Our prediction gives significant results for measuring the signal of the velocity-shape correlation in the data and therefore, we also describe two possible applications of the velocity-shape correlation.…”
Section: Introductionmentioning
confidence: 97%
See 2 more Smart Citations
“…Note that Refs. [30,58] derived the Hankel transform relations between the 3D power spectrum and the 3D correlation function for the IA shear field assuming the linear IA model and linear RSD effect. Compared to this, we have not employed any model for the projected tensor field or the underlying power spectra.…”
Section: Hankel Transformsmentioning
confidence: 99%