Emulating galaxy clustering and galaxy-galaxy lensing into the deeply non-linear regime: methodology, information, and forecasts

Citation:

Wibking BD, Salcedo AN, Weinberg DH, Garrison LH, Ferrer D, Tinker J, Eisenstein D, Metchnik M, Pinto P. Emulating galaxy clustering and galaxy-galaxy lensing into the deeply non-linear regime: methodology, information, and forecasts. Monthly Notices of the Royal Astronomical Society [Internet]. 2019;484 :989-1006.

Date Published:

March 01, 2019

Abstract:

The combination of galaxy-galaxy lensing (GGL) with galaxy clustering isone of the most promising routes to determining the amplitude of matterclustering at low redshifts. We show that extending clustering+GGLanalyses from the linear regime down to {̃ } 0.5 h^{-1} Mpc scalesincreases their constraining power considerably, even aftermarginalizing over a flexible model of non-linear galaxy bias. Using agrid of cosmological N-body simulations, we construct a Taylor-expansionemulator that predicts the galaxy autocorrelation ξgg(r) andgalaxy-matter cross-correlation ξgm(r) as a function ofσ8, Ωm, and halo occupation distribution (HOD)parameters, which are allowed to vary with large-scale environment torepresent possible effects of galaxy assembly bias. We present forecastsfor a fiducial case that corresponds to BOSS LOWZ galaxy clustering andSDSS-depth weak lensing (effective source density ̃0.3arcmin-2). Using tangential shear and projected correlationfunction measurements over 0.5 ≤ r_ p ≤ 30 h^{-1} Mpc yields a 2 percent constraint on the parameter combination σ _8Ω _ m^{0.6}, a factorof two better than a constraint that excludes non-linear scales (r_ p> 2 h^{-1} Mpc, 4 h^{-1} Mpc for γt, wp). Muchof this improvement comes from the non-linear clustering information,which breaks degeneracies among HOD parameters. Increasing the effectivesource density to 3 arcmin-2 sharpens the constraint on σ _8Ω_ m^{0.6} by a further factor of two. With robust modelling into thenon-linear regime, low-redshift measurements of matter clustering at the1-per cent level with clustering+GGL alone are well within reach ofcurrent data sets such as those provided by the Dark Energy Survey.

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