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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 use an N-body and halo occupation distribution (HOD) emulator method 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 Alens. We use this emulator to forecast cosmological constraints attainable from the GGL surface density profile ΔΣ(rp) and the projected galaxy correlation function wp, gg(rp) in the final (Year 6) DES data set over scales $$r_p=0.3\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$$. For a $$3{{\ \rm per\ cent}}$$ prior on Alens we forecast precisions of $$1.9{{\ \rm per\ cent}}$$, $$2.0{{\ \rm per\ cent}}$$, and $$1.9{{\ \rm per\ cent}}$$ on Ωm, σ8, and $$S_8 \equiv \sigma _8\Omega _m^{0.5}$$, marginalized over all halo occupation distribution (HOD) parameters as well as Alens. Adding scales $$r_p=0.3\!-\!3.0\, h^{-1} \, \mathrm{Mpc}$$ improves the S8 precision by a factor of ∼1.6 relative to a large scale ($$3.0\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$$) analysis, equivalent to increasing the survey area by a factor of ∼2.6. Sharpening the Alens prior to $$1{{\ \rm per\ cent}}$$ further improves the S8 precision to $$1.1{{\ \rm per\ cent}}$$, and it amplifies the gain from including non-linear scales. Our emulator achieves per cent-level accuracy similar to the projected DES statistical uncertainties, demonstrating the feasibility of a fully non-linear analysis. Obtaining precise parameter constraints from multiple galaxy types and from measurements that span linear and non-linear clustering offers many opportunities for internal cross-checks, which can diagnose systematics and demonstrate the robustness of cosmological results.
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Weak lensing the non-linear Lyα forest
ABSTRACT We evaluate the performance of the Lyman α forest weak gravitational lensing estimator of Metcalf et al. on forest data from hydrodynamic simulations and ray-trace simulated lensing potentials. We compare the results to those obtained from the Gaussian random field simulated Lyα forest data and lensing potentials used in previous work. We find that the estimator is able to reconstruct the lensing potentials from the more realistic data and investigate dependence on spectrum signal to noise. The non-linearity and non-Gaussianity in this forest data arising from gravitational instability and hydrodynamics causes a reduction in signal to noise by a factor of ∼2.7 for noise free data and a factor of ∼1.5 for spectra with signal to noise of order unity (comparable to current observational data). Compared to Gaussian field lensing potentials, using ray-traced potentials from N-body simulations incurs a further signal-to-noise reduction of a factor of ∼1.3 at all noise levels. The non-linearity in the forest data is also observed to increase bias in the reconstructed potentials by $$5-25{{\ \rm per\ cent}}$$, and the ray-traced lensing potential further increases the bias by $$20-30{{\ \rm per\ cent}}$$. We demonstrate methods for mitigating these issues including Gaussianization and bias correction which could be used in real observations.
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- Award ID(s):
- 1909193
- PAR ID:
- 10391995
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 519
- Issue:
- 4
- ISSN:
- 0035-8711
- Format(s):
- Medium: X Size: p. 5236-5245
- Size(s):
- p. 5236-5245
- Sponsoring Org:
- National Science Foundation
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