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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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Dark Energy Survey year 3 results: covariance modelling and its impact on parameter estimation and quality of fit
ABSTRACT We describe and test the fiducial covariance matrix model for the combined two-point function analysis of the Dark Energy Survey Year 3 (DES-Y3) data set. Using a variety of new ansatzes for covariance modelling and testing, we validate the assumptions and approximations of this model. These include the assumption of Gaussian likelihood, the trispectrum contribution to the covariance, the impact of evaluating the model at a wrong set of parameters, the impact of masking and survey geometry, deviations from Poissonian shot noise, galaxy weighting schemes, and other sub-dominant effects. We find that our covariance model is robust and that its approximations have little impact on goodness of fit and parameter estimation. The largest impact on best-fitting figure-of-merit arises from the so-called fsky approximation for dealing with finite survey area, which on average increases the χ2 between maximum posterior model and measurement by $$3.7{{\ \rm per\ cent}}$$ (Δχ2 ≈ 18.9). Standard methods to go beyond this approximation fail for DES-Y3, but we derive an approximate scheme to deal with these features. For parameter estimation, our ignorance of the exact parameters at which to evaluate our covariance model causes the dominant effect. We find that it increases the scatter of maximum posterior values for Ωm and σ8 by about $$3{{\ \rm per\ cent}}$$ and for the dark energy equation-of-state parameter by about $$5{{\ \rm per\ cent}}$$.
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- Award ID(s):
- 2009210
- PAR ID:
- 10349830
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 508
- Issue:
- 3
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- 3125 to 3165
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/mnras/stab2384
