SARCS strong-lensing galaxy groups
Bibliographic record
Abstract
We present the weak-lensing and optical analysis of the SL2S-ARCS (SARCS) sample of strong-lensing candidates. The sample is based on the Strong Lensing Legacy Survey (SL2S), a systematic search of strong-lensing systems in the photometric Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). The SARCS sample focusses on arc-like features and is designed to contain mostly galaxy groups. We briefly present the weak-lensing methodology that we used to estimate the mass of the SARCS objects. Among 126 candidates, we obtained a weak-lensing detection (at the 1σ level) for 89 objects with velocity dispersions of the singular isothermal sphere mass model (SIS) ranging from σSIS ~ 350 km s-1 to ~1000 km s-1 with an average value of σSIS ~ 600 km s-1, corresponding to a rich galaxy group (or poor cluster). From the galaxies belonging to the bright end of the group’s red sequence (Mi < −21), we derived the optical properties of the SARCS candidates. We obtained typical richnesses of N ~ 5−15 galaxies and optical luminosities of L ~ 0.5−1.5 × 1012 L⊙ (within a radius of 0.5 Mpc). We used these galaxies to compute luminosity density maps, from which a morphological classification reveals that a large fraction of the sample (~45%) are groups with a complex light distribution, either elliptical or multi-modal, suggesting that these objects are dynamically young structures. We finally combined the lensing and optical analyses to define a sample of the 80 most secure group candidates, i.e. weak-lensing detection and over-density at the lens position in the luminosity map, to remove false detections and galaxy-scale systems from the initial sample. We use this reduced sample to probe the optical scaling relations in combination with a sample of massive galaxy clusters. We detect the expected correlations over the probed range in mass with a typical scatter of ~25% in σSIS at a given richness or luminosity, making these scaling laws interesting mass proxies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 0.002 |
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".