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Record W4386377689 · doi:10.1051/0004-6361/202346747

PDRs4All

2023· article· en· W4386377689 on OpenAlexaff
E. Habart, E. Peeters, Olivier Berné, Boris Trahin, Amélie Canin, Ryan Chown, Ameek Sidhu, Dries Van De Putte, Felipe Alarcón, Ilane Schroetter, E. Dartois, S. Vicente, A. Abergel, Edwin A. Bergin, J. Bernard‐Salas, Christiaan Boersma, Émeric Bron, J. Cami, Sara Cuadrado, Daniel Dicken, M. Elyajouri, A. Fuente, J. R. Goicoechea, Karl D. Gordon, Lina Issa, Christine Joblin, Olga Kannavou, Baria Khan, Ozan Lacinbala, David Languignon, Romane Le Gal, Alexandros Maragkoudakis, Raphaël Meshaka, Yoko Okada, Takashi Onaka, Sofia Pasquini, Marc W. Pound, Massimo Robberto, M. Röllig, Bethany Schefter, Thiébaut Schirmer, Benoît Tabone, Alexander G. G. M. Tielens, Marion Zannese, N. Ysard, M.-A. Miville-Deschênes, Isabel Aleman, L. J. Allamandola, Rebecca Auchettl, G. A. Baratta, Salma Bejaoui, Partha P. Bera, J. H. Black, F. Boulanger, Jordy Bouwman, Bernhard R. Brandl, Philippe Bréchignac, Sandra Brünken, Mridusmita Buragohain, Andrew M. Burkhardt, Alessandra Candian, S. Cazaux, J. Cernicharo, M. Chabot, Shubhadip Chakraborty, Jason Champion, Sean W. J. Colgan, Ilsa R. Cooke, A. Coutens, N. L. J. Cox, Karine Demyk, Sacha Foschino, P. García-Lario, Lisseth Gavilan, Maryvonne Gérin, C. A. Gottlieb, P. Guillard, A. Gusdorf, Patrick Hartigan, Jinhua He, Eric Herbst, Liv Hornekær, C. Jäger, E. Janot-Pacheco, Michael J. Kaufman, F. Kemper, Sarah Kendrew, M. S. Kirsanova, Pamela Klaassen, Sun Kwok, Á. Labiano, Thomas S. -Y. Lai, Timothy J. Lee, B. Leflóch, Franck Le Petit, Aigen Li, H. Linz, Cameron J. Mackie, Suzanne C. Madden, Joe̋lle Mascetti, Brett A. McGuire, Pablo Merino, Elisabetta R. Micelotta, K. A. Misselt, Jon A. Morse, G. Mulas, Naslim Neelamkodan, Ryou Ohsawa, A. Omont, R. Paladini, M. E. Palumbo, Amit Pathak, Y. J. Pendleton, Annemieke Petrignani, Thomas Pino, E. Puga, Naseem Rangwala, Mathias Rapacioli, Alessandra Ricca, Julia Román-Duval, Joseph Roser, E. Roueff, Gaël Rouillé, Farid Salama, Dinalva A. Sales, Karin Sandström, P. J. Sarre, Ella Sciamma-O’Brien, K. Sellgren, S. Shenoy, D. Teyssier, Richard Thomas, Aditya Togi, Laurent Verstraete, Adolf N. Witt, A. Wootten, Henning Zettergren, Yong Zhang, Z. Zhang, Junfeng Zhen

Bibliographic record

VenueAstronomy and Astrophysics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of British ColumbiaWestern University
Fundersnot available
KeywordsPhysicsAstrophysicsAstronomy

Abstract

fetched live from OpenAlex

Context. TheJames WebbSpace Telescope (JWST) has captured the most detailed and sharpest infrared (IR) images ever taken of the inner region of the Orion Nebula, the nearest massive star formation region, and a prototypical highly irradiated dense photo-dissociation region (PDR). Aims. We investigate the fundamental interaction of far-ultraviolet (FUV) photons with molecular clouds. The transitions across the ionization front (IF), dissociation front (DF), and the molecular cloud are studied at high-angular resolution. These transitions are relevant to understanding the effects of radiative feedback from massive stars and the dominant physical and chemical processes that lead to the IR emission that JWST will detect in many Galactic and extragalactic environments. Methods. We utilized NIRCam and MIRI to obtain sub-arcsecond images over ~150″ and 42″ in key gas phase lines (e.g., Paα, Brα, [FeII] 1.64 µm, H21−0 S(1) 2.12 µm, 0–0 S(9) 4.69 µm), aromatic and aliphatic infrared bands (aromatic infrared bands at 3.3–3.4 µm, 7.7, and 11.3 µm), dust emission, and scattered light. Their emission are powerful tracers of the IF and DF, FUV radiation field and density distribution. Using NIRSpec observations the fractional contributions of lines, AIBs, and continuum emission to our NIRCam images were estimated. A very good agreement is found for the distribution and intensity of lines and AIBs between the NIRCam and NIRSpec observations. Results. Due to the proximity of the Orion Nebula and the unprecedented angular resolution of JWST, these data reveal that the molecular cloud borders are hyper structured at small angular scales of ~0.1–1″ (~0.0002–0.002 pc or ~40–400 au at 414 pc). A diverse set of features are observed such as ridges, waves, globules and photoevaporated protoplanetary disks. At the PDR atomic to molecular transition, several bright features are detected that are associated with the highly irradiated surroundings of the dense molecular condensations and embedded young star. Toward the Orion Bar PDR, a highly sculpted interface is detected with sharp edges and density increases near the IF and DF. This was predicted by previous modeling studies, but the fronts were unresolved in most tracers. The spatial distribution of the AIBs reveals that the PDR edge is steep and is followed by an extensive warm atomic layer up to the DF with multiple ridges. A complex, structured, and folded H0/H2DF surface was traced by the H2lines. This dataset was used to revisit the commonly adopted 2D PDR structure of the Orion Bar as our observations show that a 3D “terraced” geometry is required to explain the JWST observations. JWST provides us with a complete view of the PDR, all the way from the PDR edge to the substructured dense region, and this allowed us to determine, in detail, where the emission of the atomic and molecular lines, aromatic bands, and dust originate. Conclusions. This study offers an unprecedented dataset to benchmark and transform PDR physico-chemical and dynamical models for the JWST era. A fundamental step forward in our understanding of the interaction of FUV photons with molecular clouds and the role of FUV irradiation along the star formation sequence is provided.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0070.003
Open science0.0030.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.7070.693

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 head0.010
GPT teacher head0.222
Teacher spread0.212 · 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 designNot applicable
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

Citations44
Published2023
Admission routes1
Has abstractyes

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