Light Propagation in Inhomogeneous Universes. IV. Strong Lensing and Environmental Effects
Bibliographic record
Abstract
We study the gravitational lensing of high-redshift sources in a lambda cold dark matter (ΛCDM) universe. We have performed a series of ray-tracing experiments and selected a subsample of cases of strong lensing (multiple images, arcs, and Einstein rings). For each case, we identified a massive galaxy that is primarily responsible for lensing and studied how the various density inhomogeneities along the line of sight (other galaxies, background matter) affect the properties of the image. The matter located near the lensing galaxy, and physically associated with it, has a small effect. The background matter increases the magnification by a few percent at most, while nearby galaxies can increase it by up to about 10%. The effect on the image separation is even smaller. The only significant effect results from the random alignment of physically unassociated galaxies, which can increase the magnification by factors of several, create additional images, and turn arcs into rings. We conclude that the effect of environment on strong lensing is negligible in general, and might be important only in rare cases. We show that our conclusion does not depend on the radial density profile of the galaxies responsible for lensing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".