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Record W4321202093 · doi:10.3847/2041-8213/aca769

PHANGS–JWST First Results: The Influence of Stellar Clusters on Polycyclic Aromatic Hydrocarbons in Nearby Galaxies

2023· article· en· W4321202093 on OpenAlexaff
Daniel A. Dale, M. Boquien, Ashley T. Barnes, Francesco Belfiore, Frank Bigiel, Yixian Cao, Rupali Chandar, Jérémy Chastenet, Mélanie Chevance, Sinan Deger, Oleg V. Egorov, Kathryn Grasha, Brent Groves, Hamid Hassani, Kiana F. Henny, Ralf S. Klessen, Kathryn Kreckel, J. M. Diederik Kruijssen, Kirsten L. Larson, Janice Lee, Adam K. Leroy, Daizhong Liu, E. J. Murphy, Erik Rosolowsky, Karin Sandström, Eva Schinnerer, Jessica Sutter, David A. Thilker, Elizabeth J. Watkins, Bradley C. Whitmore, Thomas G. Williams

Bibliographic record

VenueThe Astrophysical Journal Letters · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Alberta
FundersSpace Telescope Science Institute
KeywordsPolycyclic aromatic hydrocarbonGalaxyChemistryAstrophysicsPhysicsEnvironmental chemistry

Abstract

fetched live from OpenAlex

Abstract We present a comparison of theoretical predictions of dust continuum and polycyclic aromatic hydrocarbon (PAH) emission with new JWST observations in three nearby galaxies: NGC 628, NGC 1365, and NGC 7496. Our analysis focuses on a total of 1063 compact stellar clusters and 2654 stellar associations previously characterized by the Hubble Space Telescope in the three galaxies. We find that the distributions and trends in the observed PAH-focused infrared colors generally agree with theoretical expectations, and that the bulk of the observations is more aligned with models of larger, ionized PAHs. These JWST data usher in a new era of probing interstellar dust and studying how the intense radiation fields near stellar clusters and associations play a role in shaping the physical properties of PAHs.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.009
GPT teacher head0.226
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2023
Admission routes1
Has abstractyes

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