Euclid preparation. Calibrated intrinsic galaxy alignments in the Euclid Flagship simulation

TL;DR

This paper presents a semi-analytic model for intrinsic galaxy alignments (IA) within the Euclid Flagship simulation, designed to quantify IA contamination in weak lensing analyses. The authors calibrate a 12-parameter misalignment framework against COSMOS shape distributions, SDSS clustering/IA constraints, and Horizon-AGN at z=1, enabling realistic -like mock catalogs spanning wide redshift and luminosity ranges. Their approach reveals generally good agreement with calibration data, but notable discrepancies at small scales for the brightest samples and substantial uncertainties due to finite volume and sample limitations. As a first application, they forecast IA contributions to end-of-m mission tomographic weak lensing, finding IA can modify the observed E-mode power by up to ~10% in some bin combinations, underscoring the need for robust IA priors in cosmological analyses.

Abstract

Intrinsic alignments of galaxies are potentially a major contaminant of cosmological analyses of weak gravitational lensing. We construct a semi-analytic model of galaxy ellipticities and alignments in the \Euclid Flagship simulation to predict this contamination in Euclid's weak lensing observations. Galaxy shapes and orientations are determined by the corresponding properties of the host haloes in the underlying $N$-body simulation, as well as the relative positions of galaxies within their halo. Alignment strengths are moderated via stochastic misalignments, separately for central and satellite galaxies and conditional on the galaxy's redshift, luminosity, and rest-frame colour. The resulting model is calibrated against galaxy ellipticity statistics from the COSMOS Survey, selected alignment measurements based on Sloan Digital Sky Survey samples, and galaxy orientations extracted from the Horizon-AGN hydrodynamic simulation at redshift $z=1$. The best-fit model has a total of 12 alignment parameters and generally reproduces the calibration data sets well within the $1σ$ statistical uncertainties of the observations and the \flagship simulation, with notable exceptions for the most luminous sub-samples on small physical scales. The statistical power of the calibration data and the volume of the single \flagship realisation are still too small to provide informative prior ranges for intrinsic alignment amplitudes in relevant galaxy samples. As a first application, we predict that \Euclid end-of-mission tomographic weak gravitational lensing two-point statistics are modified by up to order $10\,\%$ due to intrinsic alignments.

Figures (17)

• Figure 1: Redshift, magnitude, colour distributions of mock samples, used for calibrating the IA model parameters for galaxy misalignment. Contours enclose $68\%$ and $95\%$ of the distributions.
• Figure 2: Top: Distribution of the rest-frame colour $u-r \equiv M_u-M_r$ for galaxies in a -like sample, selected by $i < 24$ from COSMOS (black) and Flagship (red) across the full redshift range covered by . Center to Bottom: Joint distribution of the rest-frame colour and the absolute rest-frame magnitude in the Subaru $r$-band in three redshift bins. Contours enclose the central $10$, $40$, and $70$ percent of the distributions, while black solid and red dashed lines represent results for COSMOS and Flagship respectively. Vertical lines at $u-r=1.2$ and $u-r=1.32$ indicate the colour cuts used to define red and blue sub-samples in COSMOS and Flagship, respectively. The cut for Flagship is shifted such that the global fraction of blue galaxies matches that of COSMOS.
• Figure 3: Fraction of blue galaxies in Flagship and COSMOS, selected by the colour cuts shown in Fig. \ref{['fig:cmz_distribution_cosmos_vs_fs2']}. The dashed lines mark $\pm 10\%$ deviations from the COSMOS data.
• Figure 4: Distribution of galaxy counts per square degree ($n_{\mathrm{gal}}$) as a function of apparent $r$-band magnitude ($m_{\rm r}$, left) and redshift ($z$, right). Results are normalized by the bin widths $\Delta m_{\rm r}$ and $\Delta z$ to allow comparisons independent of bin size. Top and bottom panels show results for the SDSS Main and BOSS LOWZ samples, respectively. Black solid and red dashed lines represent observational data and the Flagship mock samples, respectively. Vertical dotted lines indicate the selection cuts in magnitude (left) and redshift (right) applied in our sample selection. The redshift cuts in Flagship coincide with those used for the observed samples.
• Figure 5: Distribution of the rest-frame colour $g-r := M_g-M_r$ rest-frame colour in the SDSS Main sample and the corresponding Flagship mock catalogue. The vertical red line shows the cut at $u-r=0.61$ where we split the mock SDSS catalogue into a red and blue sub-sample. This cut is chosen such that we reproduce the fraction of blue galaxies from J19 of $f_{\rm blue}=0.4$.