Exploiting Non-linear Scales in Galaxy-Galaxy Lensing and Galaxy Clustering: A Forecast for the Dark Energy Survey
Andr\'es N. Salcedo, David H. Weinberg, Hao-Yi Wu, Benjamin D. Wibking

TL;DR
This paper forecasts how combining galaxy-galaxy lensing and galaxy clustering in the Dark Energy Survey can improve cosmological parameter constraints by modeling non-linear scales with an advanced emulator, achieving percent-level accuracy.
Contribution
It extends an emulator method to include non-linear scales and additional parameters, enabling precise cosmological forecasts from DES data.
Findings
Forecasted 1.9-2.0% precision on key cosmological parameters
Including non-linear scales improves S8 constraints by 1.6 times
Achieved percent-level accuracy comparable to DES statistical uncertainties
Abstract
The combination of galaxy-galaxy lensing (GGL) and galaxy clustering is a powerful probe of low redshift matter clustering, especially if it is extended to the non-linear regime. To this end, we extend the N-body and halo occupation distribution (HOD) emulator method of arxiv:1907.06293 to model the redMaGiC sample of colour-selected passive galaxies in the Dark Energy Survey (DES), adding parameters that describe central galaxy incompleteness, galaxy assembly bias, and a scale-independent multiplicative lensing bias . We use this emulator to forecast cosmological constraints attainable from the GGL surface density profile and the projected galaxy correlation function in the final (Year 6) DES data set over scales Mpc. For a prior on we forecast precisions of , , and on ,…
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