📝 SummarXiv

Abstract

Intrinsic alignments of galaxies are measured and modelled to gain cosmological information, to further understand the interactions between galaxies and to mitigate their effects on gravitational weak lensing studies. Hydrodynamical simulations are often used to constrain priors or calibrate models. Therefore, obtaining the maximum amount of information possible from these simulations is imperative. In this work, we have combined the information of shapes projected over two or three axes ($x,y,z$), for intrinsic alignment signals ($w_{g+},\ \tilde{\xi}_{g+,2}$), showing a consistent gain in signal-to-noise ratio (SNR) for all cases studied using TNG300-1. The gain in SNR is found to be higher for the addition of the second projection than for the third, and higher for shapes calculated using the reduced inertia tensor rather than the simple one. The two shape samples studied, $n_\star>300$ and $\mathrm{log}(M_\star \ h/\mathrm{M_\odot})>10.5$, where the latter has a much higher signal amplitude, show similar gains in SNR when more projections are added. We also model the correlation functions with the non-linear alignment model. The SNR gains from the measurements are higher but consistent with the constraints on the non-linear alignment amplitude $A_{\rm IA}$ and galaxy bias $b_{\rm g}$. Using multiple projection axes increases SNR overall, enabling more efficient use of numerically expensive hydrodynamical simulations.