Intrinsic alignments in the FLAMINGO simulations with two-point statistics

TL;DR

This paper tackles IA contamination in weak lensing by using the FLAMINGO hydrodynamic simulations to measure the two-point IA signal, specifically the projected galaxy clustering $w_{gg}$ and position–shape correlation $w_{g+}$ for a luminous red galaxy–like sample at $z\simeq 0$. The authors jointly fit NLA and TATT models to the IA and clustering data, discovering that a mass-dependent TATT variant (TATT-M) is very strongly preferred over NLA and reduces the higher-order parameter space by expressing $A_2$ and $A_{1\delta}$ as functions of halo mass. They show that the IA amplitude $A_1$ grows with halo mass following a power law, while the higher-order terms are smaller and can be predicted from $A_1$ and halo mass; variations in baryonic feedback have little impact beyond shifts in the stellar-mass distribution. These results provide robust IA priors and modelling guidance for upcoming surveys like Euclid and LSST.

Abstract

Intrinsic alignments are a major astrophysical contaminant for next generation large-sky surveys like Euclid and LSST. Large hydrodynamic simulations are crucial for informing the alignment modelling for these surveys. We measure position-position and position-shape correlations of a Luminous Red Galaxy sample from the FLAMINGO suite of hydrodynamical simulations, measuring the alignment signal for more than 4.9 million galaxies at redshift 0. We jointly model the clustering and alignment correlations to provide the tightest constraints on the alignment amplitude to date from a hydrodynamic simulation. We find that both the Non-Linear Alignment (NLA) and the more complex Tidal Alignment Tidal Torquing (TATT) models provide good fits to the data. We compare the measured $A_1$ amplitude to observational data and find good agreement. We measure the dependence of the NLA and TATT free parameters on halo mass. We also introduce a mass-dependent TATT model, TATT-M, by finding empirical relations between the halo mass and the TATT parameters. This allows us to fit TATT with only one parameter, $A_1$, with $A_2/A_1$ being a constant and $A_{1δ}/A_1$ being a function of halo mass. Using a Bayesian approach, we find that TATT-M is very strongly preferred by the data over NLA. Using the baryonic feedback variations of the FLAMINGO simulation suite, we test whether the TATT parameters are sensitive to feedback. Variations in AGN and supernova feedback do not significantly change the alignment amplitude beyond the change associated with the dependence of galaxy stellar mass on the strength of feedback. Our results inform the IA modelling for upcoming surveys by providing guidance on model choices, priors and sensitivities to feedback.

Figures (14)

• Figure 1: The projected position-position, $w_{\mathrm{gg}}$ (left) and position-shape, $w_{\mathrm{g+}}$ (right), correlation functions for all galaxies with more than 300 stellar particles in the 2.8 Gpc$^3$ box, as a function of the projected separation, $r_{\mathrm{p}}$. The sample contains $4\,941\,492$ galaxies with stellar masses ranging from $10^{11.28}$ to $10^{13.68} \ \mathrm{M}_{\odot}$ and halo masses ($\mathrm{M}_{200\mathrm{mean}}$) ranging from $10^{12.2}$ to $10^{15.78} \ \mathrm{M}_{\odot}$. Overplotted is the best-fitting joint clustering and NLA or TATT model with the residuals in the lower panel. The gray regions show the scales excluded from the fitting. Considering scales between 5 and 100 Mpc/$h$, TATT achieves residuals within 4 per cent.
• Figure 2: The best-fitting TATT and NLA model parameters for the 2.8 Gpc sample. Only scales within 5-100 Mpc/$h$ are modelled. The posteriors of NLA and TATT are consistent with each other.
• Figure 3: The alignment amplitude with NLA for different r-band luminosity bins, $L_0$ is the pivot luminosity, with a value of $4.6\times 10^{10} \ h^{-2} \ \mathrm{L}_{\odot}$. The galaxy sample from the 2.8 Gpc$^3$ run at $z = 0$ was first binned in stellar mass. The mean halo mass for each bin was then converted to an r-band luminosity using a fit to the data in Table 4 of Mandelbaum2006a. The FLAMINGO values, shown in deep blue, agree reasonably well with observational studies of LRGs, and extends the range to higher luminosities. Error bars show the 1$\sigma$ error on the best-fitting $A_1$. Error bars are omitted for the x-axis.
• Figure 4: Variation of the non-linear bias parameters $b_1$ and the NLA alignment amplitude $A_1$ with halo mass $M_{\mathrm{h}}$ (in our case, we use $\mathrm{M}_{200\mathrm{mean}}$ as the mass of the halo) for our galaxy sample. $b_2$ is not shown for the sake of clarity. Error bars on the bias parameter and amplitude were calculated by taking the 68th percentile values of the IA posteriors.
• Figure 5: Variation of the TATT alignment amplitudes $A_1$, $A_2$ and $A_{1\delta}$ with halo mass $M_{\mathrm{h}}$ (in our case, we use $\mathrm{M}_{200\mathrm{mean}}$ as the mass of the halo) for our central galaxy sample. Error bars on the bias parameter and amplitude were calculated by taking the 68th percentile values of the IA posteriors. Overplotted in green is the prediction for $A_2$ and $A_{1\delta}$ from our TATT-M model fit with error bars. Informed by the fits to the halo sample (Fig. \ref{['fig:TATT-M_ITs']}), we assume the relations in Eqns. \ref{['eqn:a2_a1']} and \ref{['eqn:a1d_a1']} for the galaxy sample. In orange is the fit to the galaxy sample directly, which we call TATT-M$^\prime$.