Preprint typeset using L ATEX style emulateapj v. 11/26/04 THE SL2S GALAXY-SCALE GRAVITATIONAL LENS SAMPLE. I. THE ALIGNMENT OF MASS AND LIGHT IN MASSIVE EARLY-TYPE GALAXIES AT Z = 0.2 − 0.9
Bibliographic record
Abstract
We study the relative alignment of mass and light in a sample of 16 massive early-type galaxies at z = 0.2 − 0.9 that act as strong gravitational lenses. The sample was identified from deep multiband images obtained as part of the Canada France Hawaii Telescope Legacy Survey as part of the Strong Lensing Legacy Survey (SL2S). Higher resolution follow-up imaging is available for a subset of 10 systems. We construct gravitational lens models and infer total enclosed mass, elongation, and position angle of the mass distribution. By comparison with the observed distribution of light we infer that there is a substantial amount of external shear with mean value 〈γext 〉 = 0.12 ± 0.05, arising most likely from the environment of the SL2S lenses. In a companion paper, we combine these measurements with follow-up Keck spectroscopy to study the evolution of the stellar and dark matter content of early-type galaxies as a function of cosmic time. Subject headings: galaxies: fundamental parameters — gravitational lensing: strong lenging to measure accurately the relative distribution of baryonic and dark matter on sub-galactic scales. By-andlarge, traditional methods require the presence of a luminous
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.595 | 0.383 |
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".