Models of gravitational lens candidates from Space Warps CFHTLS
Bibliographic record
Abstract
We report modelling follow-up of recently discovered gravitational-lens candidates in the Canada France Hawaii Telescope Legacy Survey. Lens modelling was done by a small group of specially interested volunteers from the Space Warps citizen–science community who originally found the candidate lenses. Models are categorized according to seven diagnostics indicating (a) the image morphology and how clear or indistinct it is, (b) whether the mass map and synthetic lensed image appear to be plausible, and (c) how the lens-model mass compares with the stellar mass and the abundance-matched halo mass. The lensing masses range from ∼1011 to >1013 M⊙. Preliminary estimates of the stellar masses show a smaller spread in stellar mass (except for two lenses): a factor of a few below or above ∼1011 M⊙. Therefore, we expect the stellar-to-total mass fraction to decline sharply as lensing mass increases. The most massive system with a convincing model is J1434+522 (SW 05). The two low-mass outliers are J0206−095 (SW 19) and J2217+015 (SW 42); if these two are indeed lenses, they probe an interesting regime of very low star formation efficiency. Some improvements to the modelling software (SpaghettiLens), and discussion of strategies regarding scaling to future surveys with more and frequent discoveries, are included.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".