The halo model as a versatile tool to predict intrinsic alignments

Abstract

Intrinsic alignments (IAs) of galaxies are an important contaminant for cosmic shear studies, but the modelling is complicated by the dependence of the signal on the source galaxy sample. In this paper, we use the halo model formalism to capture this diversity and examine its implications for Stage-III and Stage-IV cosmic shear surveys. We account for the different IA signatures at large and small scales, as well as for the different contributions from central/satellite and red/blue galaxies, and we use realistic mocks to account for the characteristics of the galaxy populations as a function of redshift. We inform our model using the most recent observational findings: we include a luminosity dependence at both large and small scales and a radial dependence of the signal within the halo. We predict the impact of the total IA signal on the lensing angular power spectra, including the current uncertainties from the IA best-fits to illustrate the range of possible impact on the lensing signal: the lack of constraints for fainter galaxies is the main source of uncertainty for our predictions of the IA signal. We investigate how well effective models with limited degrees of freedom can account for the complexity of the IA signal. Although these lead to negligible biases for Stage-III surveys, we find that, for Stage-IV surveys, it is essential to at least include an additional parameter to capture the redshift dependence.

Strong detection of the CMB lensing and galaxy weak lensing cross-correlation from ACT-DR4,PlanckLegacy, and KiDS-1000

“…We consider an intrinsic alignment amplitude of A IA = 0.54 as our fiducial value in Eq. ( 11), as motivated by Fortuna et al (2021), and find A N Planck = 0.56 ± 0.19 for the northern region from ACT×KiDS-N, and A S Planck = 0.84 ± 0.19 in the south from Planck×KiDS-S. The amplitude fits from these to patches differ by ∼1.5σ which is consistent with the previous results that use data from different parts of the sky such as the CFHTLenS/RCSLenS analysis (Harnois-Déraps et al 2016).…”

“…For the cosmological analysis we use a Gaussian prior centred on A IA = 0.54 with a width of σ A IA = 0.65. This prior captures the upper and lower values of A IA from the KiDS-1000 cosmic shear analysis and the cases presented in Fortuna et al (2021).…”

“…where A IA is a constant that controls the amplitude of the crosscorrelation, the constant C 1 absorbs the dimensions of the normalisation such that we set C 1 ρ c = 0.0134, (the same value is adopted in Fortuna et al 2021) and D(z) is the growth factor (Joachimi et al 2011). Given the relatively low S/N of our measurement, we ignore any redshift or luminosity dependence and simply fit for the A IA term.…”

“…To be able to relate these measurements to the fainter and high redshift galaxy samples used in analyses like this one requires the correct modelling to account for the difference in the blue/red galaxy fraction and also the satellite galaxy fraction. We therefore adopt a wide prior motivated by the model presented in Fortuna et al (2021); a detailed discussion is presented in Appendix A. For the cosmological analysis we use a Gaussian prior centred on A IA = 0.54 with a width of σ A IA = 0.65.…”

“…In this appendix we motivate the choice of prior for the parameter A IA , which controls the amplitude of the intrinsic alignment (IA). To do so, we make use of the model presented in Fortuna et al (2021), which allows us to predict the IA signal for a KiDS-like survey using parameter constraints inferred from recent IA measurements. Relevant for the discussion here is the treatment of the large-scale alignment signal.…”