Sampling the Faraday rotation sky of TNG50: imprint of the magnetized circumgalactic medium around Milky Way-like galaxies
Bibliographic record
Abstract
ABSTRACT Faraday rotation measure (RM) is arguably the most practical observational tracer of magnetic fields in the diffuse circumgalactic medium (CGM). We sample synthetic Faraday rotation skies of Milky Way-like galaxies in TNG50 of the IllustrisTNG project by placing an observer inside the galaxies at a solar circle-like position. Our synthetic RM grids emulate specifications of current and upcoming surveys; the NRAO VLA Sky Survey (NVSS), the Polarisation Sky Survey of the Universe’s Magnetism (POSSUM), and a future Square Kilometre Array (SKA1-mid) polarization survey. It has been suggested that magnetic fields regulate the survival of high-velocity clouds. However, there is only a small number of observational detections of magnetized clouds thus far. In the first part of the paper, we test conditions for the detection of magnetized circumgalactic clouds. Based on the synthetic RM samplings of clouds in the simulations, we predict upcoming polarimetric surveys will open opportunities for the detection of even low-mass and distant clouds. In the second part of the paper, we investigate the imprint of the CGM in the all-sky RM distribution. We test whether the RM variation produced by the CGM is correlated with global galaxy properties, such as distance to a satellite, specific star formation rate, neutral hydrogen covering fraction, and accretion rate to the supermassive black hole. We argue that the observed fluctuation in the RM measurements on scales less than 1○, which has been considered an indication of intergalactic magnetic fields, might in fact incorporate a significant contribution of the Milky Way CGM.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".