On the relation between magnetic field strength and gas density in the interstellar medium: a multiscale analysis
Bibliographic record
Abstract
ABSTRACT The relationship between magnetic field strength B and gas density n in the interstellar medium is of fundamental importance. We present and compare Bayesian analyses of the B–n relation for two comprehensive observational data sets: a Zeeman data set and 700 observations using the Davis–Chandrasekhar–Fermi (DCF) method. Using a hierarchical Bayesian analysis we present a general, multiscale broken power-law relation, $B=B_0(n/n_0)^{\alpha }$, with $\alpha =\alpha _1$ for $n< n_0$ and $\alpha _2$ for $n>n_0$, and with $B_0$ the field strength at $n_0$. For the Zeeman data, we find: $\alpha _1={0.15^{+0.06}_{-0.09}}$ for diffuse gas and $\alpha _2 = {0.53^{+0.09}_{-0.07}}$ for dense gas with $n_0 = 0.40^{+1.30}_{-0.30}\times 10^4$ cm$^{-3}$. For the DCF data, we find: $\alpha _1={0.26^{+0.01}_{-0.01}}$ and $\alpha _2={0.77_{-0.15}^{+0.14}}$, with $n_0=14.00^{+10.00}_{-7.00}\times 10^4$ cm$^{-3}$, where the uncertainties give 68 per cent credible intervals. We perform a similar analysis on nineteen numerical magnetohydrodynamic simulations covering a wide range of physical conditions from protostellar discs to dwarf and Milky Way-like galaxies, computed with the arepo, flash, pencil, and ramses codes. The resulting exponents depend on several physical factors such as dynamo effects and their time-scales, turbulence, and initial seed field strength. We find that the dwarf and Milky Way-like galaxy simulations produce results closest to the observations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".