Cross-correlation cosmic shear with the SDSS and VLA FIRST surveys
Bibliographic record
Abstract
We measure the cosmic shear power spectrum on large angular scales by cross-correlating the shapes of ∼9 million galaxies measured in the optical Sloan Digital Sky Survey (SDSS) with the shapes of ∼2.7 × 105 radio galaxies measured by the overlapping Very Large Array FIRST (Faint Images of the Radio Sky at Twenty centimetres) survey. Our measurements span the multipole range 10 < ℓ < 130, corresponding to angular scales 2° < θ < 20°. On these scales, the shear maps from both surveys suffer from significant systematic effects that prohibit a measurement of the shear power spectrum from either survey alone. Conversely, we demonstrate that a power spectrum measured by cross-correlating the two surveys is unbiased, reducing the impact of the systematics by at least an order of magnitude. We measure an E-mode power spectrum from the data that is inconsistent with zero signal at the 99 per cent confidence (∼2.7σ) level. The B-mode and EB cross-correlations are both found to be consistent with zero (within 1σ). These constraints are obtained after a careful error analysis that accounts for uncertainties due to cosmic variance, random galaxy shape noise and shape measurement errors, as well as additional errors associated with the observed large-scale systematic effects in the two surveys. Our constraints are consistent with the expected signal in the concordance cosmological model assuming recent estimates of the cosmological parameters from the Planck satellite, and literature values for the median redshifts of the SDSS and FIRST galaxy populations. The cross-correlation approach will be ideal for extracting robust results, with the exquisite control of systematics required, from future cosmic shear surveys with the Square Kilometre Array, Large Synoptic Survey Telescope, Euclid and WFIRST-AFTA.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".