Radio emission from supernova remnants: implications for post-shock magnetic field amplification & the magnetic fields of galaxies
Bibliographic record
Abstract
Using observational data from the literature, we show that the non-thermal radio luminosity (L) of supernova remnants (SNRs) is a strong function of the average gas surface density (Σg) of the galaxy in which the remnants reside, from normal spirals to dense luminous starbursts. Our result supports the interpretation of the radio sources in M82 and Arp 220 as normal SNRs, and not ‘radio’ supernovae (SNe). We combine a simple theory for electron cooling in SNRs with their observed radio luminosities to estimate the remnant magnetic field strength (BSNR): the correlation between L and Σg implies that BSNR also increases with Σg. We explore two interpretations of this correlation: (1) BSNR is generated by post-shock magnetic field amplification, with B2SNR∝Σg and (2) BSNR results from shock compression of the ambient interstellar medium (ISM) magnetic field (BISM), with BISM being larger in denser galaxies. We find that shock compression is, on average, sufficient to produce the observed radio emission from SNRs in the densest starburst galaxies; amplification of post-shock magnetic fields is not required. By contrast, in normal spirals modest post-shock field amplification in some remnants (a factor of ∼ few − 10) is consistent with the data; we find tentative evidence that both the Alfvén speed within SNRs and the ratio of B2SNR/8π to the post-shock pressure (‘εB’) are constant in SNRs from galaxy to galaxy. We discuss observational tests that can be used to more definitively distinguish between these two interpretations of the radio luminosities of SNRs. Regardless of which is correct, the radio emission from SNRs provides an upper limit to BISM that is independent of the minimum energy assumption. For the densest starbursts, the magnetic energy density in the ISM is below the total ISM pressure required for hydrostatic equilibrium; thus magnetic fields are not dynamically important on the largest scales in starbursts, in contrast with spiral galaxies like our own. This dichotomy may have implications for galactic dynamo theory.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".