Magnetic field evolution in cosmic filaments with LOFAR data
Bibliographic record
Abstract
ABSTRACT Measuring the magnetic field in cosmic filaments reveals how the Universe is magnetized and the process that magnetized it. Using the Rotation Measures (RM) at 144 MHz from the LoTSS DR2 data, we analyse the rms of the RM extragalactic component as a function of redshift to investigate the evolution with redshift of the magnetic field in filaments. From previous results, we find that the extragalactic term of the RM rms at 144 MHz is dominated by the contribution from filaments (more than 90 per cent). Including an error term to account for the minor contribution local to the sources, we fit the data with a model of the physical filament magnetic field, evolving as $B_f = B_{f,0}\, (1+z)^\alpha$ and with a density drawn from cosmological simulations of five magnetogenesis scenarios. We find that the best-fitting slope is in the range α = [ − 0.2, 0.1] with uncertainty of σα = 0.4–0.5, which is consistent with no evolution. The comoving field decreases with redshift with a slope of γ = α − 2 = [ − 2.2, −1.9]. The mean field strength at z = 0 is in the range Bf, 0 = 39–84 nG. For a typical filament gas overdensity of δg = 10 the filament field strength at z = 0 is in the range $B_{f,0}^{10}=8$–26 nG. A primordial stochastic magnetic field model with initial comoving field of BMpc = 0.04–0.11 nG is favoured. The primordial uniform field model is rejected.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".