Bibliographic record
Abstract
Context. The new generation of deep wide field optical imaging like the Canada France Hawaii Telescope Legacy Survey (CFHTLS) enables discoveries of all types of gravitational lenses present in the sky. The Strong Lensing Legacy Survey (SL2S) project has started an inventory, respectively for clusters or groups of galaxies lenses, and for Einstein rings around distant massive ellipticals. Aims. Here we attempt to extend this inventory by finding lensing events produced by massive edge-on spiral galaxies which remains a poorly documented class of lenses. Methods. We implement and test an automated search procedure of edge-on galaxy lenses in the CFHTLS Wide field with magnitude 18 < i < 21, inclination angle lower than 25 ◦ and having a photometric redshift determination. The procedure estimates the lensing convergence of each galaxy from the R band Tully-Fisher law and selects only the few candidates which display a possible nearby arc configuration at a radius compatible with this convergence (rarc < 2rE). The efficiency of the procedure is tested after a visual examination of the whole initial sample of 30 444 individual edge-on spirals. Results. We calculate the surface density of edge-on lenses possibly detected in a survey for a given seeing. We deduce that this theoretical number is about 10 for the CFHTLS Wide, a number compatible with the 3 good candidates detected during this work. We show that the efficiency of the Tully-Fisher selection criterium in selecting lens candidates depends crucially on the accuracy of lens photometric redshift. Eventually, we argue that the LSST will detect at least a hundred of such lens candidates. Key words. Gravitational lensing: strong – Surveys – dark matter – Galaxies: spiral – Galaxies: halos 1.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".