Discovery of magnetic fields along stacked cosmic filaments as revealed by radio and X-ray emission
Bibliographic record
Abstract
ABSTRACT Diffuse filaments connect galaxy clusters to form the cosmic web. Detecting these filaments could yield information on the magnetic field strength, cosmic ray population, and temperature of intercluster gas; yet, the faint and large-scale nature of these bridges makes direct detections very challenging. Using multiple independent all-sky radio and X-ray maps we stack pairs of luminous red galaxies as tracers for cluster pairs. For the first time, we detect an average surface brightness between the clusters from synchrotron (radio) and thermal (X-ray) emission with ≳5σ significance, on physical scales larger than observed to date (${\ge}3$ Mpc). We obtain a synchrotron spectral index of α ≃ −1.0 and estimates of the average magnetic field strength of $30\,\mathrm{ nG} \le B \le 60 $ nG, derived from both equipartition and inverse-Compton arguments, implying a 5–15 per cent degree of field regularity when compared with Faraday rotation measure estimates. While the X-ray detection is inline with predictions, the average radio signal comes out higher than predicted by cosmological simulations and dark matter annihilation and decay models. This discovery demonstrates that there are connective structures between mass concentrations that are significantly magnetized, and the presence of sufficient cosmic rays to produce detectable synchrotron radiation.
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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) | 0.002 | 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".