The signature of substructure on gravitational lensing in the $\mathsf{\Lambda}$CDM cosmological model
Bibliographic record
Abstract
We present a study of the lens properties of quadruply imaged systems, lensed by numerically simulated galaxies. We investigate a simulated elliptical and disc galaxy drawn from high resolution simulations of galaxy formation in a concordance ΛCDM universe. The simulations include the effects of gas dynamics, star formation and feedback processes. Flux-ratio anomalies observed in strong gravitational lensing potentially provide an indicator for the presence of mass substructure in lens galaxies as predicted from CDM simulations. We particularly concentrate on the prediction that, for an ideal cusp caustic, the sum of the signed magnifications of the three highly magnified images should vanish when the source approaches the cusp. Strong violation of this cusp relation indicates the presence of substructure, regardless of the global, smooth mass model of the lens galaxy. We draw the following conclusions: (1) the level of substructure present in simulations produces violations of the cusp relation comparable to those observed; (2) higher-order catastrophes (e.g. swallowtails) can also cause changes of the order of 0.6 in the cusp relation as predicted by a smooth model; (3) the flux anomaly distribution depends on the image parity and flux and both the brightest minimum and saddle-point images are more affected by substructure than the fainter images. In addition, the brightest saddle point is demagnified w.r.t. the brightest minimum. Our results are fully numerical and properly include all mass scales, without making semi-analytic assumptions. They are ultimately limited by the mass resolution of single particles in the simulation determined by current computational limits, however show that our results are not affected by shot-noise due to the finite number of particles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".