PHANGS-JWST First Results: Tracing the Diffuse ISM with JWST Imaging of Polycyclic Aromatic Hydrocarbon Emission in Nearby Galaxies
Bibliographic record
Abstract
JWST observations of polycyclic aromatic hydrocarbon (PAH) emission provide some of the deepest and highest resolution views of the cold interstellar medium (ISM) in nearby galaxies. If PAHs are well mixed with the atomic and molecular gas and illuminated by the average diffuse interstellar radiation field, PAH emission may provide an approximately linear, high resolution, high sensitivity tracer of diffuse gas surface density. We present a pilot study that explores using PAH emission in this way based on MIRI observations of IC 5332, NGC 628, NGC 1365, and NGC 7496 from the PHANGS-JWST Treasury. Using scaling relationships calibrated in Leroy et al. (2022), scaled F1130W provides 10--40 pc resolution and 3$σ$ sensitivity of $Σ_{\rm gas} \sim 2$ M$_\odot$ pc$^{-2}$. We characterize the surface densities of structures seen at $< 7$ M$_\odot$ pc$^{-2}$ in our targets, where we expect the gas to be HI-dominated. We highlight the existence of filaments, inter-arm emission, and holes in the diffuse ISM at these low surface densities. Below $\sim 10$ M$_\odot$ pc$^{-2}$ for NGC 628, NGC 1365, and NGC 7496 the gas distribution shows a ``Swiss cheese''-like topology due to holes and bubbles pervading the relatively smooth distribution of diffuse ISM. Comparing to recent galaxy simulations, we observe similar topology for the low surface density gas, though with notable variations between simulations with different setups and resolution. Such a comparison of high resolution, low surface density gas with simulations is not possible with existing atomic and molecular gas maps, highlighting the unique power of JWST maps of PAH emission.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".