Polycyclic Aromatic Hydrocarbon Emission in Spitzer/IRS Maps. II. A Direct Link between Band Profiles and the Radiation Field Strength
Bibliographic record
Abstract
Abstract We decompose the observed 7.7 μm polycyclic aromatic hydrocarbon (PAH) emission complexes in a large sample of over 7000 mid-infrared spectra of the interstellar medium using spectral cubes observed with the Spitzer/IRS-SL instrument. In order to fit the 7.7 μm PAH emission complex we invoke four Gaussian components, which are found to be very stable in terms of their peak positions and widths across all of our spectra, and subsequently define a decomposition with fixed parameters, which gives an acceptable fit for all the spectra. We see a strong environmental dependence on the interrelationships between our band fluxes—in the H ii regions all four components are intercorrelated, while in the reflection nebulae (RNs) the inner and outer pairs of bands correlate in the same manner as previously seen for NGC 2023. We show that this effect arises because the maps of RNs are dominated by emission from strongly irradiated photodissociation regions, while the much larger maps of H ii regions are dominated by emission from regions much more distant from the exciting stars, leading to subtly different spectral behavior. Further investigation of this dichotomy reveals that the ratio of two of these components (centered at 7.6 and 7.8 μm) is linearly related to the UV-field intensity (log G 0). We find that this relationship does not hold for sources consisting of circumstellar material, which are known to have variable 7.7 μm spectral profiles.
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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 | 0.002 | 0.001 |
| 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".