CHANG-ES – VI. Probing Supernova energy deposition in spiral galaxies through multiwavelength relationships
Bibliographic record
Abstract
How a galaxy regulates its supernovae (SNe) energy into different interstellar/circumgalactic medium components strongly affects galaxy evolution. Based on the JVLA D-configuration C- (6 GHz) and L-band (1.6 GHz) continuum observations, we perform statistical analysis comparing multiwavelength properties of the Continuum Haloes in Nearby Galaxies – an EVLA Survey galaxies. The high-quality JVLA data and edge-on orientation enable us for the first time to include the halo into the energy budget for a complete radio-flux-limited sample. We find tight correlations of Lradio with the mid-IR-based star formation rate (SFR). The normalization of our I1.6 GHz/W Hz−1–SFR relation is ∼2–3times of those obtained for face-on galaxies, probably a result of enhanced IR extinction at high inclination. We also find tight correlations between Lradio and the SNe energy injection rate |$\dot{E}_{\rm SN(Ia+CC)}$|, indicating the energy loss via synchrotron radio continuum accounts for ∼1 of |$\dot{E}_{\rm SN}$|, comparable to the energy contained in cosmic ray electrons. The integrated C-to-L-band spectral index is α ∼ 0.5–1.1 for non-active galactic nucleus galaxies, indicating a dominance by the diffuse synchrotron component. The low-scatter Lradio–SFR/|$L_{\rm radio}\text{-}\dot{E}_{\rm SN (Ia+CC)}$| relationships have superlinear logarithmic slopes at ∼2σ in L band (1.132 ± 0.067/1.175 ± 0.102) while consistent with linear in C band (1.057 ± 0.075/1.100 ± 0.123). The superlinearity could be naturally reproduced with non-calorimeter models for galaxy discs. Using Chandra halo X-ray measurements, we find sublinear LX–Lradio relations. These results indicate that the observed radio halo of a starburst galaxy is close to electron calorimeter, and a galaxy with higher SFR tends to distribute an increased fraction of SNe energy into radio emission (than X-ray).
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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 | 0.001 | 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) | 0.003 | 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".