On the complementarity of galaxy clustering with cosmic shear and flux magnification
Bibliographic record
Abstract
With the wealth of forthcoming data from wide-field surveys, it is more important than ever to understand the full range of independent probes of cosmology at our disposal. Here, we explore the potential for galaxy clustering and cosmic shear, separately and in combination, including the effects of lensing magnification. We show that inferred cosmological parameters may be biased when flux magnification is neglected. Results are presented for Stage III ground-based and Stage IV space-based photometric surveys, using slopes of the luminosity function inferred from the Canada–France–Hawaii Lensing Survey catalogue. We find that combining with clustering improves the shear Dark Energy Task Force-like Figure of Merit by a factor of 1.33 using only autocorrelations in redshift for the clustering analysis, rising to 1.52 when cross-correlations are also included. The further addition of galaxy–galaxy lensing gives increases in the shear Figure of Merit by a factor of 2.82 and 3.7 for each type of clustering analysis, respectively. The presence of flux magnification in a clustering analysis does not significantly affect the precision of cosmological constraints when combined with cosmic shear and galaxy–galaxy lensing. However, if magnification is neglected, inferred cosmological parameter values are biased, with biases in some cosmological parameters larger than statistical errors.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".