A principal component analysis of polycyclic aromatic hydrocarbon emission in NGC 2023
Bibliographic record
Abstract
ABSTRACT We use the measured fluxes of polycyclic aromatic hydrocarbon (PAH) emission features at 6.2, 7.7, 8.6, 11.0, and 11.2 μm in the reflection nebula NGC 2023 to carry out a principal component analysis (PCA) as a means to study previously reported variations in the PAH emission. We find that almost all of the variations (99 per cent) can be explained with just two parameters – the first two principal components (PCs). We explore the characteristics of these PCs and show that the first PC (PC1), which is the primary driver of the variation, represents the amount of emission of a mixture of PAHs with ionized species dominating over neutral species. The second PC (PC2) traces variations in the ionization state of the PAHs across the nebula. Correlations of the PCs with various PAH ratios show that the 6.2 and 7.7 μm bands behave differently than the 8.6 and 11.0 μm bands, thereby forming two distinct groups of ionized bands. We compare the spatial distribution of the PCs to the physical conditions, in particular to the strength of the radiation field, G0, and the G0/nH ratio and find that the variations in PC2, i.e. the ionization state of PAHs are strongly affected by G0 whereas the amount of PAH emission (as traced by PC1) does not depend on G0.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".