CHANG-ES. XXXII. Spatially Resolved Thermal–Nonthermal Separation from Radio Data Alone—New Probes into NGC 3044 and NGC 5775
Bibliographic record
Abstract
Abstract We have carried out spatially resolved thermal–nonthermal separation on two edge-on galaxies, NGC 3044 and NGC 5775, using only radio data. Narrowband imaging within a frequency band that is almost contiguous from 1.25 to 7.02 GHz (L band, S band, and C band) has allowed us to fit spectra and construct thermal, nonthermal, and nonthermal spectral index maps. This method does not require any ancillary Hα and IR data or rely on dust corrections that are challenging in edge-on galaxies. For NGC 3044, at 15″ resolution, we find a median thermal fraction of ∼13% with an estimated uncertainty in this fraction of ∼50% at 4.13 GHz. This compares well with the Hα mixture method results. We uncovered evidence for a vertical outflow feature reaching at least z ∼ 3.5 kpc in projection above the plane, reminiscent of M82's starburst wind. For the higher star formation rate galaxy, NGC 5775, at 12″ resolution, we find a median thermal fraction of 44% at 4.13 GHz with an estimated error on this fraction of 17%. Both galaxies show a change of slope (flattening) in L band. These results suggest that a radio-only method for separating thermal from nonthermal emission is not only feasible, but able to reveal new features that might otherwise be obscured in edge-on disks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".