ON THE CONTRIBUTION OF LARGE-SCALE STRUCTURE TO STRONG GRAVITATIONAL LENSING
Bibliographic record
Abstract
We study the correlation between the locations of galaxy–galaxy strong-lensing candidates and tracers of large-scale structure from both weak lensing (WL) or X-ray emission. The Cosmological Evolution Survey (COSMOS) is a unique data set, combining deep, high resolution and contiguous imaging in which strong lenses have been discovered, plus unparalleled multiwavelength coverage. To help interpret the COSMOS data, we have also produced mock COSMOS strong- and WL observations, based on ray-tracing through the Millennium Simulation. In agreement with the simulations, we find that strongly lensed images with the largest angular separations are found in the densest regions of the COSMOS field. This is explained by a prevalence among the lens population in dense environments of elliptical galaxies with high total-to-stellar mass ratios, which can deflect light through larger angles. However, we also find that the overall fraction of elliptical galaxies with strong gravitational lensing is independent of the local mass density; this observation is not true of the simulations, which predict an increasing fraction of strong lenses in dense environments. The discrepancy may be a real effect, but could also be explained by various limitations of our analysis. For example, our visual search of strong lens systems could be incomplete and suffer from selection bias; the luminosity function of elliptical galaxies may differ between our real and simulated data; or the simplifying assumptions and approximations used in our lensing simulations may be inadequate. Work is therefore ongoing. Automated searches for strong lens systems will be particularly important in better constraining the selection function.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".