Exploring the magnetized cosmic web through low-frequency radio emission
Bibliographic record
Abstract
Recent improvements in the capabilities of low-frequency radio telescopes provide a unique opportunity to study thermal and non-thermal properties of the cosmic web. We argue that the diffuse, polarized emission from giant radio relics traces structure formation shock waves and illuminates the large-scale magnetic field. To show this, we model the population of shock-accelerated relativistic electrons in high-resolution cosmological simulations of galaxy clusters and calculate the resulting radio synchrotron emission. We find that individual shock waves correspond to localized peaks in the radio surface brightness map which enables us to measure Mach numbers for these shocks. We show that the luminosities and number counts of the relics strongly depend on the magnetic field properties, the cluster mass and dynamical state. By suitably combining different cluster data, including Faraday rotation measures, we are able to constrain some macroscopic parameters of the plasma at the structure formation shocks, such as models of turbulence. We also predict upper limits for the properties of the warm-hot intergalactic medium, such as its temperature and density. We predict that the current generation of radio telescopes [Low-Frequency Array (LOFAR), Giant Meter Radio Telescope (GMRT), the Murchison Wide-field Array (MWA) and Long Wavelength Array (LWA)] have the potential to discover a substantially larger sample of radio relics, with multiple relics expected for each violently merging cluster. Future experiments [(Square Kilometre Array (SKA)] should enable us to further probe the macroscopic parameters of plasma physics in clusters.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".