Probing cluster magnetism with embedded and background radio sources in Planck clusters
Bibliographic record
Abstract
Magnetic fields remain an elusive part of the content of galaxy clusters. Faraday rotation and depolarisation of extragalactic radio sources are useful probes, but the limited availability of polarised radio sources necessitates the stacking of clusters to study average magnetic field properties. We recently presented a Karl G. Jansky Very Large Array survey of the 124 most massive Planck clusters at low redshift ( z < 0.35), finding a clear depolarisation trend with the cluster impact parameter, with sources at smaller projected distances to the cluster centre showing more depolarisation. In this study, we combine the depolarisation information with the observed rotation measure (RM) and present an investigation of the average magnetic field properties of the sample, using both background sources and sources embedded in clusters. We observe a significant increase in the RM scatter, σ RRM , closer to the cluster centres. Averaging all 124 clusters, we find a scatter within R 500 of σ RRM = 209 ± 37 rad m −2 , with background sources and cluster members showing similar values (200 ± 33 and 219 ± 66 rad m −2 , respectively). In the simple assumption of a uniform amplitude magnetic field with a single fluctuation scale Λ c , this translates to an average magnetic field strength of 2 (Λ c /10 kpc) −0.5 μG. The profile of σ RRM as a function of the projected radius is inconsistent with a model that has a simple scaling B ∝ n e η , with an observed deficit near the centre of clusters possibly caused by the fact that the highest RM sources near the centre of clusters are depolarised. Combining depolarisation and RM in a full forward model, we find that the magnetic field power spectrum roughly agrees with the Kolmogorov value, but that none of the Gaussian random field models can fully explain the observed relatively flat profiles. This implies that more sophisticated models of cluster magnetic fields in a cosmological context are needed.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".