PDRs4All
Bibliographic record
Abstract
Aromatic infrared bands (AIBs) are a set of broad emission bands at 3.3, 6.2, 7.7, 8.6, 11.2, and 12.7 μm, seen in the infrared spectra of most galaxies. With the James Webb Space Telescope (JWST), the 3.3 μm AIB can in principle be detected up to a redshift of ~7. Relating the evolution of the 3.3 μm AIB to local physical properties of the interstellar medium (ISM) is thus of paramount importance. By applying a dedicated machine learning algorithm to JWST NIRSpec observations of the Orion Bar photodissociation region obtained as part of the PDRs4All Early Release Science (ERS) program, we extracted two template spectra capturing the evolution of the AIB-related emission in the 3.2–3.6 μm range, which includes the AIB at 3.3 μm and its main satellite band at 3.4 μm. In the Orion Bar, we analyzed the spatial distribution of the templates and their relationship with the ro-vibrational H2 line at 2.12 μm, the pure rotational line of H2 at 4.69 μm and the Pfund δ line at 3.29 μm. We find that one template (AIBIrrad) traces regions of neutral atomic gas with strong far-UV fields, while the other template (AIBShielded) corresponds to shielded regions with lower FUV fields and a higher molecular gas fraction. We then show that these two templates can be used to fit the NIRSpec AIB-related spectra of nearby galaxies. The relative weight of the two templates (AIBIrrad/Shielded) is a tracer of the radiative feedback from massive stars on the ISM. We derive an estimate of AIBIrrad/Shielded in a z = 4.22 lensed galaxy and find that it has a lower value than for local galaxies. This pilot study illustrates how a detailed analysis of AIB emission in nearby regions can be used to probe the physical conditions of the extragalactic ISM.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.047 |
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".