Cross-correlation of gravitational lensing from DES Science Verification data with SPT and<i>Planck</i>lensing
Bibliographic record
Abstract
We measure the cross-correlation between weak lensing of galaxy images and of the cosmic microwave background (CMB). The effects of gravitational lensing on different sources will be correlated if the lensing is caused by the same mass fluctuations. We use galaxy shape measurements from 139 deg2 of the Dark Energy Survey (DES) Science Verification data and overlapping CMB lensing from the South Pole Telescope (SPT) and Planck. The DES source galaxies have a median redshift of zmed ∼ 0.7, while the CMB lensing kernel is broad and peaks at z ∼ 2. The resulting cross-correlation is maximally sensitive to mass fluctuations at z ∼ 0.44. Assuming the Planck 2015 best-fitting cosmology, the amplitude of the DES×SPT cross-power is found to be ASPT = 0.88 ± 0.30 and that from DES×Planck to be APlanck = 0.86 ± 0.39, where A = 1 corresponds to the theoretical prediction. These are consistent with the expected signal and correspond to significances of 2.9σ and 2.2σ, respectively. We demonstrate that our results are robust to a number of important systematic effects including the shear measurement method, estimator choice, photo-z uncertainty and CMB lensing systematics. We calculate a value of A = 1.08 ± 0.36 for DES×SPT when we correct the observations with a simple intrinsic alignment model. With three measurements of this cross-correlation now existing in the literature, there is not yet reliable evidence for any deviation from the expected LCDM level of cross-correlation. We provide forecasts for the expected signal-to-noise ratio of the combination of the five-year DES survey and SPT-3G.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".