Constraints on thermal conductivity in the merging cluster Abell 2146
Bibliographic record
Abstract
ABSTRACT The cluster of galaxies Abell 2146 is undergoing a major merger and is an ideal cluster to study intracluster medium (ICM) physics, as it has a simple geometry with the merger axis in the plane of the sky, its distance allows us to resolve features across the relevant scales and its temperature lies within Chandra’s sensitivity. Gas from the cool core of the subcluster has been partially stripped into a tail of gas, which gives a unique opportunity to look at the survival of such gas and determine the rate of conduction in the ICM. We use deep 2.4-Ms Chandra observations of Abell 2146 to produce a high spatial resolution map of the temperature structure along a plume in the ram-pressure stripped tail, described by a partial cone, which is distinguishable from the hot ambient gas. Previous studies of conduction in the ICM typically rely on estimates of the survival time for key structures, such as cold fronts. Here we use detailed hydrodynamical simulations of Abell 2146 to determine the flow velocities along the stripped plume and measure the time-scale of the temperature increase along its length. We find that conduction must be highly suppressed by multiple orders of magnitude compared to the Spitzer rate, as the energy used is about 1 per cent of the energy available. We discuss magnetic draping around the core as a possible mechanism for suppressing conduction.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".