Dark Energy Survey Year 1 Results: Tomographic cross-correlations between Dark Energy Survey galaxies and CMB lensing from South Pole <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>Telescope</mml:mi><mml:mo>+</mml:mo><mml:mi>Planck</mml:mi></mml:mrow></mml:math>
Bibliographic record
Abstract
We measure the cross-correlation between redMaGiC galaxies selected from the Dark Energy Survey (DES) year 1 data and gravitational lensing of the cosmic microwave background (CMB) reconstructed from South Pole Telescope (SPT) and Planck data over $1289\text{ }\text{ }{\mathrm{deg}}^{2}$. When combining measurements across multiple galaxy redshift bins spanning the redshift range of $0.15<z<0.90$, we reject the hypothesis of no correlation at $19.9\ensuremath{\sigma}$ significance. When removing small-scale data points where thermal Sunyaev-Zel'dovich signal and nonlinear galaxy bias could potentially bias our results, the detection significance is reduced to $9.9\ensuremath{\sigma}$. We perform a joint analysis of galaxy-CMB lensing cross-correlations and galaxy clustering to constrain cosmology, finding ${\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}=0.27{6}_{\ensuremath{-}0.030}^{+0.029}$ and ${S}_{8}={\ensuremath{\sigma}}_{8}\sqrt{{\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}/0.3}=0.80{0}_{\ensuremath{-}0.094}^{+0.090}$. We also perform two alternate analyses aimed at constraining only the growth rate of cosmic structure as a function of redshift, finding consistency with predictions from the concordance $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model. The measurements presented here are part of a joint cosmological analysis that combines galaxy clustering, galaxy lensing and CMB lensing using data from DES, SPT and Planck.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".