Intrinsic alignments of galaxies in the MassiveBlack-II simulation: analysis of two-point statistics

Abstract

The intrinsic alignment of galaxies with the large-scale density field is an important astrophysical contaminant in upcoming weak lensing surveys. We present detailed measurements of the galaxy intrinsic alignments and associated ellipticity-direction (ED) and projected shape (w g+ ) correlation functions for galaxies in the cosmological hydrodynamic MassiveBlack-II (MB-II) simulation. We carefully assess the effects on galaxy shapes, misalignment of the stellar component with the dark matter shape and two-point statistics of iterative weighted (by mass and luminosity) definitions of the (reduced and unreduced) inertia tensor. We find that iterative procedures must be adopted for a reliable measurement of the reduced tensor but that luminosity versus mass weighting has only negligible effects. Both ED and w g+ correlations increase in amplitude with subhalo mass (in the range of 10 10 − 6.0 × 10 14 h −1 M ), with a weak redshift dependence (from z = 1 to z = 0.06) at fixed mass. At z ∼ 0.3, we predict a w g+ that is in reasonable agreement with SDSS LRG measurements and that decreases in amplitude by a factor of ∼ 5-18 for galaxies in the LSST survey. We also compared the intrinsic alignments of centrals and satellites, with clear detection of satellite radial alignments within their host halos. Finally, we show that w g+ (using subhalos as tracers of density) and w δ+ (using dark matter density) predictions from the simulations agree with that of non-linear alignment models (NLA) at scales where the 2-halo term dominates in the correlations (and tabulate associated NLA fitting parameters). The 1-halo term induces a scale dependent bias at small scales which is not modeled in the NLA model.

The impact of AGN feedback on galaxy intrinsic alignments in the Horizon simulations

“…Although intrinsic alignments can generally be described analytically in the linear regime by assuming the intrinsic shape of a galaxy is correlated with the tidal field of the large-scale structure (Catelan et al 2001a;Blazek et al 2011), the actual strength of alignment and its nonlinear behaviour are sensitive to the properties of galaxies. Observational studies using LRGs have found the alignment amplitude to be luminosity-dependent, with brighter galaxies showing stronger alignments (Hirata et al 2007a;Joachimi et al 2011) in qualitative agreement with cosmological simulations (Tenneti et al 2015a;Chisari et al 2015). Moreover, the alignment signal has been shown to depend on galaxy color and on the region of the galaxy that is being probed by the shape measurement (Chisari et al 2015;Singh & Mandelbaum 2016;Georgiou et al 2019a,b;Samuroff et al 2018).…”

Intrinsic alignment of redMaPPer clusters: cluster shape–matter density correlation

“…Velliscig et al (2015) measured the IA signal in the EAGLE simulations (Schaye et al 2015) and found that, when all star particles in each halo were used to estimate the shapes of galaxies, the IA signal of a LRG-like sample was overpredicted; however, by only using star particles inside a radius that contained half the stellar mass of the halo, the observations could roughly be matched. Tenneti et al (2015) measured the IA signal of a LRG-like sample in the MassiveBlack-II simulations and reported good agreement with observations for SDSS LRGs; however, they used a reduced inertia tensor to define the galaxy ellipticities, downweighting particles further away from the centre, effectively similar to what was done in Velliscig et al (2015). It is not yet clear how the shapes of galaxies in hydrodynamical simulations compare to the shapes of LRGs that are measured in the data.…”

The alignment and shape of dark matter, stellar, and hot gas distributions in the EAGLE and cosmo-OWLS simulations

“…However, the actual algorithms used to retrieve this shape can differ substantially and unfortunately the corresponding results do not often agree (see Zemp et al 2011, for an analysis of this problem under controlled conditions with known shapes). Most notably, results on the shape of a particle distribution may vary if one adopts the inertia tensor rather than the reduced inertia tensor, or some iterative form of the two (see discussions in Zemp et al 2011;Tenneti et al 2015). The differences between the inertia tensor and the reduced inertia tensor are driven by the fact that in the reduced inertia tensor calculation particles are not weighted by their distance from the centre.…”