Modeling Intrinsic Galaxy Alignment in the MICE Simulation

Kai Hoffmann, Lucas F. Secco, Jonathan Blazek, Martin Crocce, Pau Tallada-Crespí, Simon Samuroff, Judit Prat, Jorge Carretero, Pablo Fosalba, Enrique Gaztanaga, Francisco J. Castander

Submitted on 28 June 2022


The intrinsic alignment (IA) of galaxies is potentially a major limitation in deriving cosmological constraints from weak lensing surveys. In order to investigate this effect we assign intrinsic shapes and orientations to galaxies in the light-cone output of the MICE simulation, spanning 5000deg2 and reaching redshift z=1.4. This assignment is based on a 'semi-analytic' IA model that uses photometric properties of galaxies as well as the spin and shape of their host halos. Advancing on previous work, we include more realistic distributions of galaxy shapes and a luminosity dependent galaxy-halo alignment. The IA model parameters are calibrated against COSMOS and BOSS LOWZ observations. The null detection of IA in observations of blue galaxies is accounted for by setting random orientations for these objects. We compare the two-point alignment statistics measured in the simulation against predictions from the analytical IA models NLA and TATT over a wide range of scales, redshifts and luminosities for red and blue galaxies separately. We find that both models fit the measurements well at scales above 8h1Mpc, while TATT outperforms NLA at smaller scales. The IA parameters derived from our fits are in broad agreement with various observational constraints from red galaxies. Lastly, we build a realistic source sample, mimicking DES Year 3 observations and use it to predict the IA contamination to the observed shear statistics. We find this prediction to be within the measurement uncertainty, which might be a consequence of the random alignment of blue galaxies in the simulation.


Comment: 33 pages, 27 figures, 3 Tables, submitted to PRD

Subject: Astrophysics - Cosmology and Nongalactic Astrophysics