PDRs4All VI: Probing the Photochemical Evolution of PAHs in the Orion Bar Using Machine Learning Techniques
Bibliographic record
Abstract
[Abridged] JWST observations of the Orion Bar have shown the incredible richness of PAH bands and their variation on small scales. We aim to probe the photochemical evolution of PAHs across the key zones of the photodissociation region (PDR) that is the Orion Bar using unsupervised machine learning. We use NIRSpec and MIRI IFU data from the JWST ERS Program PDRs4All. We lever bisecting k-means clustering to generate detailed spatial maps of the spectral variability in several wavelength regions. We discuss the variations in the cluster profiles and connect them to the local physical conditions. We interpret these variations with respect to the key zones: the HII region, the atomic PDR zone, and the three dissociation fronts. The PAH emission exhibits spectral variation that depends strongly on spatial position in the PDR. We find the 8.6um band to behave differently than all other bands which vary systematically with one another. We find uniform variation in the 3.4-3.6um bands and 3.4/3.3 intensity ratio. We attribute the carrier of the 3.4-3.6um bands to a single side group attached to very similarly sized PAHs. Cluster profiles reveal a transition between characteristic profiles classes of the 11.2um feature from the atomic to the molecular PDR zone. We find the carriers of each of the profile classes to be independent, and reason the latter to be PAH clusters existing solely deep in the molecular PDR. Clustering also reveals a connection between the 11.2 and 6.2um bands; and that clusters generated from variation in the 10.9-11.63um region can be used to recover those in the 5.95-6.6um region. Clustering is a powerful tool for characterizing PAH variability on both spatial and spectral scales. For individual bands as well as global spectral behaviours, we find UV-processing to be the most important driver of the evolution of PAHs and their spectral signatures in the Orion Bar.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".