CHANG-ES X: Spatially Resolved Separation of Thermal Contribution from Radio Continuum Emission in Edge-on Galaxies
Bibliographic record
Abstract
Abstract We analyze the application of star formation rate calibrations using Hα and 22 μm infrared (IR) imaging data in predicting the thermal radio component for a test sample of three edge-on galaxies (NGC 891, NGC 3044, and NGC 4631) in the Continuum Halos in Nearby Galaxies—an EVLA Survey (CHANG-ES). We use a mixture of Hα and 24 μm calibration from Calzetti et al. and a linear 22 μm only calibration from Jarrett et al. on the test sample. We apply these relations on a pixel-to-pixel basis to create thermal prediction maps in the two CHANG-ES bands: L and C band (1.5 GHz and 6.0 GHz, respectively). We analyze the resulting nonthermal spectral index maps, and find a characteristic steepening of the nonthermal spectral index with vertical distance from the disk after application of all methods. We find possible evidence of extinction in the 22 μm data as compared to 70 μm Spitzer Multiband Imaging Photometer imaging in NGC 891. We analyze a larger sample of edge-on and face-on galaxy 25–100 μm flux ratios, and find that the ratios for edge-ons are systematically lower by a factor of 1.36, a result we attribute to excess extinction in the mid-IR in edge-ons. We introduce a new calibration for correcting the Hα luminosity for dust when galaxies are edge-on or very dusty.
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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 | 0.000 |
| 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) | 0.001 | 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".