The mass distribution of the unusual merging cluster Abell 2146 from strong lensing
Bibliographic record
Abstract
Abell 2146 consists of two galaxy clusters that have recently collided close to the plane of the sky, and it is unique in showing two large shocks on $\textit{Chandra X-ray Observatory}$ images. With an early stage merger, shortly after first core passage, one would expect the cluster galaxies and the dark matter to be leading the X-ray emitting plasma. In this regard, the cluster Abell 2146-A is very unusual in that the X-ray cool core appears to lead, rather than lag, the brightest cluster galaxy (BCG) in their trajectories. Here we present a strong-lensing analysis of multiple-image systems identified on $\textit{Hubble Space Telescope}$ images. In particular, we focus on the distribution of mass in Abell 2146-A in order to determine the centroid of the dark matter halo. We use object colours and morphologies to identify multiple-image systems; very conservatively, four of these systems are used as constraints on a lens mass model. We find that the centroid of the dark matter halo, constrained using the strongly lensed features, is coincident with the BCG, with an offset of ≈2 kpc between the centres of the dark matter halo and the BCG. Thus from the strong-lensing model, the X-ray cool core also leads the centroid of the dark matter in Abell 2146-A, with an offset of ≈30 kpc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".