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Dark Energy Survey Year 3 Results: Optimizing the Lens Sample in Combined Galaxy Clustering and Galaxy-Galaxy Lensing Analysis

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arxiv 2011.03411 v3 pith:KHVA2JZF submitted 2020-11-06 astro-ph.CO

Dark Energy Survey Year 3 Results: Optimizing the Lens Sample in Combined Galaxy Clustering and Galaxy-Galaxy Lensing Analysis

classification astro-ph.CO
keywords samplegainsredshiftgalaxyomegaphotometricselectionsigma
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We investigate potential gains in cosmological constraints from the combination of galaxy clustering and galaxy-galaxy lensing by optimizing the lens galaxy sample selection using information from Dark Energy Survey (DES) Year 3 data and assuming the DES Year 1 Metacalibration sample for the sources. We explore easily reproducible selections based on magnitude cuts in $i$-band as a function of (photometric) redshift, $z_{\rm phot}$, and benchmark the potential gains against those using the well established redMaGiC sample. We focus on the balance between density and photometric redshift accuracy, while marginalizing over a realistic set of cosmological and systematic parameters. Our optimal selection, the MagLim sample, satisfies $i < 4 \, z_{\rm phot} + 18$ and has $\sim 30\%$ wider redshift distributions but $\sim 3.5$ times more galaxies than redMaGiC. Assuming a wCDM model and equivalent scale cuts to mitigate nonlinear effects, this leads to $40\%$ increase in the figure of merit for the pair combinations of $\Omega_m$, $w$, and $\sigma_8$, and gains of $16\%$ in $\sigma_8$, $10\%$ in $\Omega_m$, and $12\%$ in $w$. Similarly, in LCDM we find an improvement of $19\%$ and $27\%$ on $\sigma_8$ and $\Omega_m$, respectively. We also explore flux-limited samples with a flat magnitude cut finding that the optimal selection, $i < 22.2$, has $\sim 7$ times more galaxies and $\sim 20\%$ wider redshift distributions compared to MagLim, but slightly worse constraints. We show that our results are robust with respect to the assumed galaxy bias and photometric redshift uncertainties with only moderate further gains from increased number of tomographic bins or the inclusion of bin cross-correlations, except in the case of the flux-limited sample, for which these gains are more significant.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Dark Energy Survey Year 3 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing

    astro-ph.CO 2021-05 accept novelty 6.0

    DES Y3 3x2pt analysis constrains S8=0.776±0.017 and Ωm=0.339±0.032 in flat ΛCDM, consistent with Planck CMB results at p=0.13-0.48.

  2. Taming Additive Systematics via Redshift-Bin-Optimized Star-Galaxy Separation

    astro-ph.CO 2026-05 unverdicted novelty 5.0

    Redshift-bin-optimized color cuts using unWISE photometry reduce stellar contamination in the DES Y3 MagLim lens sample by 1.3-5.5% varying across bins and footprint.

  3. Cosmology with HI Intensity Mapping

    astro-ph.CO 2026-07 accept novelty 4.0

    SKAO HI intensity mapping forecasts yield competitive LambdaCDM constraints (e.g. H0 to ~0.3 km/s/Mpc optimistic) via power spectrum, BAO, bispectrum and stacking, complementary to CMB and optical surveys.

  4. Galaxy and halo angular clustering in LCDM and Modified Gravity cosmologies

    astro-ph.CO 2022-04 unverdicted novelty 4.0

    N-body light-cone mocks show 2-4 sigma deviations in third-order angular clustering between LCDM and f(R)/nDGP models at z=0.15-0.3 for halos and galaxies, with stronger signals in the dark-matter field.

  5. Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts

    astro-ph.IM 2026-05 unverdicted novelty 3.0

    AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.