The detection of cluster magnetic fields via radio source depolarisation
Bibliographic record
Abstract
It has been well established that galaxy clusters have magnetic fields. The exact properties and origin of these magnetic fields are still uncertain even though these fields play a key role in many astrophysical processes. Various attempts have been made to derive the magnetic field strength and structure of nearby galaxy clusters using Faraday rotation of extended cluster radio sources. This approach needs to make various assumptions that could be circumvented when using background radio sources. However, because the number of polarised radio sources behind clusters is low, at the moment such a study can only be done statistically. In this paper, we investigate the depolarisation of radio sources inside and behind clusters in a sample of 124 massive clusters at z < 0.35 observed with the Karl G. Jansky Very Large Array. We detect a clear depolarisation trend with the cluster impact parameter, with sources at smaller projected distances to the cluster centre showing more depolarisation. By combining the radio observations with ancillary X-ray data from Chandra , we compare the observed depolarisation with expectations from cluster magnetic field models using individual cluster density profiles. The best-fitting models have a central magnetic field strength of 5−10 μG with power-law indices between n = 1 and n = 4. We find no strong difference in the depolarisation trend between sources embedded in clusters and background sources located at similar projected radii, although the central region of clusters is still poorly probed by background sources. We also examine the depolarisation trend as a function of cluster properties such as the dynamical state, mass, and redshift. We see a hint that dynamically disturbed clusters show more depolarisation than relaxed clusters in the r > 0.2 R 500 region. In the core region, we did not observe enough sources to detect a significant difference between cool-core and non-cool-core clusters. Our findings show that the statistical depolarisation of radio sources is a good probe of cluster magnetic field parameters. Cluster members can be used for this purpose as well as background sources because the local interaction between the radio galaxies and the intracluster medium does not strongly affect the observed depolarisation trend.
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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 | 0.002 | 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".