A SIMPLE CONNECTION BETWEEN THE NEAR- AND MID-INFRARED EMISSION OF GALAXIES AND THEIR STAR FORMATION RATES
Bibliographic record
Abstract
We have measured the near-infrared colors and the fluxes of individual pixels in 68 galaxies common to the Spitzer Infrared Nearby Galaxies Survey and the Large Galaxy Atlas Survey. Pixels from each galaxy are grouped into regions of increasingly red near-infrared colors. As expected, the majority of pixels are shown to have relatively constant NIR flux ratios (log 10 I 3.6 /I 1.25 = −0.30 ± 0.07 and log 10 I 4.5 /I 3.6 = −0.19 ± 0.02), representing the blackbody continuum emission of main sequence stars. However, pixels with red NIR colors correspond to pixels with higher H α emission and dust extinction. We show that the NIR colors are correlated to both quantities, with the strongest correlation to the intrinsic H α emission. In addition, in regions of high star formation, the average intensity of pixels in red-excess regions (at 1.25 μm, 3.6 μm, 4.5 μm, 5.6 μm, 8.0 μm and 24 μm) scales linearly with the intrinsic intensity of Hα emission, and thus with the star formation rate (SFR) within the pixel. This suggests that most NIR-excess regions are not red because their light is being depleted by absorption. Instead, they are red because additional infrared light is being contributed by a process linked to star formation. This is surprising because the shorter wavelength bands in our study (1.25 μm–5.6 μm) do not probe emission from cold (10–20 K) and warm (50–100 K) dust associated with star formation in molecular clouds. However, emission from hot dust (700–1000 K) and/or polycyclic aromatic hydrocarbon (PAH) molecules can explain the additional emission seen at the shorter wavelengths in our study. The contribution from hot dust and/or PAH emission at 2 μm–5 μm and PAH emission at 5.6 μm and 8.0 μm scales linearly with warm dust emission at 24 μm and the intrinsic Hα emission. Since both are tied to the SFR, our analysis shows that the NIR excess continuum emission and PAH emission at ∼1–8 μm can be added to spectral energy distribution models in a very straightforward way, by simply adding an additional component to the models that scales linearly with SFR.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".