Euclid preparation. Testing analytic models of galaxy intrinsic alignments in the Euclid Flagship simulation

TL;DR

This study tackles intrinsic alignments as a key systematic in weak lensing by testing the Non Linear Alignment (NLA) and Tidal Alignment and Tidal Torquing (TATT) models within the Euclid Flagship simulation. It develops IA mock data with realistic galaxy shapes, colors, and luminosities, and measures projected IA statistics $w_{gg}$, $w_{g+}$, $w_{++}$ across redshift $0.1<z<2.1$ to constrain IA parameters. The authors find that both NLA and TATT can describe the IA signal down to $r_p\approx6$–$7\,h^{-1}$Mpc, with red galaxies showing IA amplitudes consistent with observational trends and blue galaxies showing weaker or consistent-with-zero alignments depending on redshift, while the redshift evolution requires luminosity dependence for a good match. These results demonstrate that Flagship is a valuable tool for forecasting IA contamination in Euclid-like samples and for informing priors in future cosmological analyses, such as 3$\times$2pt, where accurate IA modeling is essential to avoid biases in cosmological inferences.

Abstract

We model intrinsic alignments (IA) in Euclid's Flagship simulation to investigate its impact on Euclid's weak lensing signal. Our IA implementation in the Flagship simulation takes into account photometric properties of galaxies as well as their dark matter host halos. We compare simulations against theory predictions, determining the parameters of two of the most widely used IA models: the Non Linear Alignment (NLA) and the Tidal Alignment and Tidal Torquing (TATT) models. We measure the amplitude of the simulated IA signal as a function of galaxy magnitude and colour in the redshift range $0.1<z<2.1$. We find that both NLA and TATT can accurately describe the IA signal in the simulation down to scales of $6$-$7 \,h^{-1}\,$Mpc. We measure alignment amplitudes for red galaxies comparable to those of the observations, with samples not used in the calibration procedure. For blue galaxies, our constraints are consistent with zero alignments in our first redshift bin $0.1 < z < 0.3$, but we detect a non-negligible signal at higher redshift, which is, however, consistent with the upper limits set by observational constraints. Additionally, several hydrodynamical simulations predict alignment for spiral galaxies, in agreement with our findings. Finally, the evolution of alignment with redshift is realistic and comparable to that determined in the observations. However, we find that the commonly adopted redshift power-law for IA fails to reproduce the simulation alignments above $z=1.1$. A significantly better agreement is obtained when a luminosity dependence is included, capturing the intrinsic luminosity evolution with redshift in magnitude-limited surveys. We conclude that the Flagship IA simulation is a useful tool for translating current IA constraints into predictions for IA contamination of Euclid-like samples.

Figures (18)

• Figure 1: Flagship normalised redshift distribution. This distribution is computed for galaxies with visible magnitude $\IE < 24.5$. The two continuous vertical lines represent the range of our analysis, $0.1 < z < 2.1$, while each dashed line delimits the redshift bins we considered.
• Figure 2: Colour-magnitude diagram of Flagship galaxies at redshift $z=0.2$ and $z=0.8$. We present the 68, 95, and 99$\%$ percentiles of the distributions. The dashed vertical line represents the cut that we used to define red and blue galaxy samples, i.e. $u-r = 1.32$. We shifted the rest-frame magnitude by $-4$ at redshift $z=0.8$ for clarity.
• Figure 3: Example of IA measurements. The black points with error bars correspond to the measurements of $w_{\mathrm{g}+}$ (top) and $w_{++}$ (bottom), for the brightest red sample ($M_r = -22.8)$ in the redshift slice $z \in [0.7, 0.9]$ in the Flagship simulation. The solid red lines correspond to the best fit using the TATT model given in Eqs. \ref{['eq:TATT1']} and \ref{['eq:TATT2']}. The shaded area corresponds to the scales that we do not use in the fitting procedure.
• Figure 4: Example of intrinsic alignment power spectra. The power spectra are computed with the Flagship cosmology at an arbitrary redshift $z = 0.7$. The values of the IA parameters are $A_1 = 2$, $A_2 = 1$, and $b_{\mathrm{TA}}=1.$
• Figure 5: Illustration of the sample selection implemented in Flagship. The lightcone is divided into redshift slices of size $\Delta z = 0.2$. From each slice, we constructed a density sample from which we computed $w_{\mathrm{gg}}$ and a variety of shape samples from which we computed projected IA statistics.