Mapping the Dark Matter Using Weak Lensing
Bibliographic record
Abstract
Weak gravitational lensing of distant galaxies by foreground structures has proven to be a powerful tool to study the mass distribution in the universe. The advent of panoramic cameras on 4-m class telescopes has led to a first generation of surveys that already compete with large redshift surveys in terms of the accuracy with which cosmological parameters can be determined. The next surveys, which already have started taking data, will provide another major step forward. At the current level, systematics appear under control, and it is expected that weak lensing will develop into a key tool in the era of precision cosmology, provided we improve our knowledge of the non-linear matter power spectrum and the source redshift distribution. In this review we will briefly describe the principles of weak lensing and discuss the results of recent cosmic shear surveys. We show how the combination of weak lensing and cosmic microwave background measurements can provide tight constraints on cosmological parameters. We also demonstrate the usefulness of weak lensing in studies of the relation between the galaxy distribution and the underlying dark matter distribution (“galaxy biasing”), which can provide important constraints on models of galaxy formation. Finally, we discuss new and upcoming large cosmic shear surveys.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".