Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and galaxy-galaxy lensing using the MagLim lens sample
Bibliographic record
Abstract
The cosmological information extracted from photometric surveys is most robust when multiple probes of the large scale structure of the Universe are used. Two of the most sensitive probes are the clustering of galaxies and the tangential shear of background galaxy shapes produced by those foreground galaxies, so-called galaxy-galaxy lensing. Combining the measurements of these two two-point functions leads to cosmological constraints that are independent of the way galaxies trace matter (the galaxy bias factor). The optimal choice of foreground, or lens, galaxies is governed by the joint, but conflicting requirements to obtain accurate redshift information and large statistics. We present cosmological results from the full $5000\text{ }\text{ }{\mathrm{deg}}^{2}$ of the Dark Energy Survey's first three years of observations (Y3) combining those two-point functions, using for the first time a magnitude-limited lens sample (MagLim) of 11 million galaxies, especially selected to optimize such combination, and 100 million background shapes. We consider two flat cosmological models, the Standard Model with dark energy and cold dark matter ($\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$) a variation with a free parameter for the dark energy equation of state ($w\mathrm{CDM}$). Both models are marginalized over 25 astrophysical and systematic nuisance parameters. In $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ we obtain for the matter density ${\mathrm{\ensuremath{\Omega}}}_{m}={0.320}_{\ensuremath{-}0.034}^{+0.041}$ and for the clustering amplitude ${S}_{8}\ensuremath{\equiv}{\ensuremath{\sigma}}_{8}({\mathrm{\ensuremath{\Omega}}}_{m}/0.3{)}^{0.5}={0.778}_{\ensuremath{-}0.031}^{+0.037}$, at 68% C.L. The latter is only $1\ensuremath{\sigma}$ smaller than the prediction in this model informed by measurements of the cosmic microwave background by the Planck satellite. In $w\mathrm{CDM}$ we find ${\mathrm{\ensuremath{\Omega}}}_{m}={0.32}_{\ensuremath{-}0.046}^{+0.044}$, ${S}_{8}={0.777}_{\ensuremath{-}0.051}^{+0.049}$ and dark energy equation of state $w=\ensuremath{-}{1.031}_{\ensuremath{-}0.379}^{+0.218}$. We find that including smaller scales, while marginalizing over nonlinear galaxy bias, improves the constraining power in the ${\mathrm{\ensuremath{\Omega}}}_{m}\ensuremath{-}{S}_{8}$ plane by 31% and in the ${\mathrm{\ensuremath{\Omega}}}_{m}\ensuremath{-}w$ plane by 41% while yielding consistent cosmological parameters from those in the linear bias case. These results are combined with those from cosmic shear in a companion paper to present full DES-Y3 constraints from the three two-point functions ($3\ifmmode\times\else\texttimes\fi{}2\mathrm{pt}$).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".