Tying Spatial Variations in Polycyclic Aromatic Hydrocarbon (PAH) Emission to a Changing PAH Population in the Reflection Nebula NGC 2023
Bibliographic record
Abstract
Abstract The PAH emission in Spitzer-IRS spectral maps of the reflection nebula NGC 2023 have been previously studied using a Gaussian decomposition method for the 7–9 μm region and a database-fitting approach. Both studies provided insight into the spatial-spectral evolution of the PAH population and related them to changing local physical conditions. This study investigates whether the database-fitting technique provides insight into the PAH populations at the origin of the four Gaussian components. To this end, clustered PAH species maps and spectra are generated from the database-fitting results using spectral clustering utilizing the Structural Similarity Index as an affinity measure. The application of spectral clustering solely based on spatial structure is strongly dependent on the anatomy of the considered regions and is unable to align specific morphological features with a PAH population characterized by a single distinct property. However, in the south FOV the projected distance from the star of the peak emission in a cluster map correlates with the PAH cation fraction and the cluster dominated by small PAHs is confined to the S and SSE ridges, consistent with results from Knight et al. Furthermore, the cluster and Gaussian maps exhibit limited morphological similarity and the 7–9 μm cluster spectra do not show consistent overlap with any of the Gaussian components. However, the relative strengths of the Gaussian components strongly correlate with the PAH ionization parameter as determined from the database-fitting approach. This lends further support to the existence of at least two sub-populations contributing to the 7–9 μm PAH emission.
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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) | 0.001 | 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".