[no title]
Bibliographic record
Abstract
We present a new strong lensing analysis of the galaxy cluster MACS J1206.2−0847 (MACS 1206), at z = 0.44, using deep spectroscopy from CLASH-VLT and VLT/MUSE archival data in combination with imaging from the Cluster Lensing and Supernova survey with Hubble. MUSE observations enable the spectroscopic identification of 23 new multiply imaged sources, extending the previous compilations by a factor of approximately five. In total, we use the positional measurements of 82 spectroscopic multiple images belonging to 27 families at z = 1.0−6.1 to reconstruct the projected total mass distribution of MACS 1206. Remarkably, 11 multiple images are found within 50 kpc of the brightest cluster galaxy, making this an unprecedented set of constraints for the innermost projected mass distribution of a galaxy cluster. We thus find that, although dynamically relaxed, the smooth matter component (dark matter plus hot gas) of MACS 1206 shows a significant asymmetry, which closely follows the asymmetric distribution of the stellar component (galaxy members and intracluster light). We determine the value of the innermost logarithmic slope of the projected total mass density profile and find it to be close to the canonical Navarro-Frenk-White value. We demonstrate that this quantity is very robust against different parametrizations of the diffuse mass component;however, this is not the case when only one central image is used in the mass reconstruction. We also show that the mass density profile from our new strong lensing model is in very good agreement with dynamical and X-ray measurements at larger radii, where they overlap.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".