Astrometric Perturbations in Substructure Lensing
Bibliographic record
Abstract
In recent years gravitational lensing has been used as a means to detect substructure in galaxy-sized halos, via anomalous flux ratios in quadruply imaged lenses. In addition to causing anomalous flux ratios, substructure may also perturb the positions of lensed images at observable levels. In this paper we numerically investigate the scale of such astrometric perturbations using realistic models of substructure distributions. Substructure distributions that project clumps near the Einstein radius of the lens result in perturbations that are the least degenerate with the best-fit smooth macromodel, with residuals at the milliarcsecond scale. Degeneracies between the center of the lens potential and astrometric perturbations suggest that milliarcsecond constraints on the center of the lensing potential boost the observed astrometric perturbations by an order of magnitude compared to leaving the center of the lens as a free parameter. In addition, we discuss methods of substructure detection via astrometric perturbations that avoid full lens modeling in favor of local image observables and also discuss modeling of systems with luminous satellites to constrain the masses of those satellites.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".