Probing dark matter with adaptive-optics based flux ratio anomalies: photometric and astrometric precision
Bibliographic record
Abstract
ABSTRACT Strong gravitational lensing is a powerful probe of the distribution of matter on sub-kpc scales. It can be used to test the existence of completely dark sub-haloes surrounding galaxies, as predicted by the standard cold dark matter model, or to test alternative dark matter models. The constraining power of the method depends strongly on photometric and astrometric precision and accuracy. We simulate and quantify the capabilities of upcoming adaptive optics systems and advanced instruments on ground-based telescopes, focusing as an illustration on the Keck Telescope (OSIRIS + KAPA, LIGER + KAPA) and the Thirty Meter Telescope (TMT; IRIS + NFIRAOS). We show that these new systems will achieve dramatic improvements over current ones in both photometric and astrometric precision. Narrow line flux ratio errors below 2 per cent, and submilliarcsecond astrometric precision will be attainable for typical quadruply imaged quasars. With TMT, the exposure times required will be of order a few minutes per system, enabling the follow-up of 100–1000 systems expected to be discovered by the Rubin, Euclid, and Roman Telescopes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".