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Record W4210480907 · doi:10.1051/0004-6361/202142315

Stellar feedback in M83 as observed with MUSE

2022· article· en· W4210480907 on OpenAlexafffund
Lorenza Della Bruna, Angela Adamo, P. Amram, Erik Rosolowsky, Christopher Usher, Mattia Sirressi, Andreas Schruba, Éric Emsellem, Adam K. Leroy, Arjan Bik, William P. Blair, Anna F. McLeod, Göran Östlin, Florent Renaud, Carmelle Robert, Laurie Rousseau-Nepton, L. J. Smith

Bibliographic record

VenueAstronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversité LavalUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Science FoundationSwedish National Space AgencyVetenskapsrådetEuropean Southern ObservatoryKnut och Alice Wallenbergs StiftelseCanadian Institute for Theoretical Astrophysics
KeywordsPhysicsAstrophysicsRedshiftGalaxyStarsVelocity dispersionAstronomySpiral galaxyStar formationLuminosity

Abstract

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Context. Young massive stars inject energy and momentum into the surrounding gas, creating a multi-phase interstellar medium (ISM) and regulating further star formation. The main challenge of studying stellar feedback proves to be the variety of scales spanned by this phenomenon, ranging from the immediate surrounding of the stars (H II regions, 10s pc scales) to galactic-wide kiloparsec scales. Aims. We present a large mosaic (3.8 × 3.8 kpc) of the nearby spiral galaxy M83, obtained with the MUSE instrument at ESO Very Large Telescope. The integral field spectroscopy data cover a large portion of the optical disk at a resolution of ∼20 pc, allowing the characterisation of single H II regions while sampling diverse dynamical regions in the galaxy. Methods. We obtained the kinematics of the stars and ionised gas, and compared them with molecular gas kinematics observed in CO(2-1) with the ALMA telescope array. We separated the ionised gas into H II regions and diffuse ionised gas (DIG) and investigated how the fraction of Hα luminosity originating from the DIG (fDIG) varies with galactic radius. Results. We observe that both stars and gas trace the galactic disk rotation, as well as a fast-rotating nuclear component (30″ ≃ 700 pc in diameter), likely connected to secular processes driven by the galactic bar. In the gas kinematics, we observe a stream east of the nucleus (50″ ≃ 1250 pc in size), redshifted with respect to the disk. The stream is surrounded by an extended ionised gas region (1000 × 1600 pc) with enhanced velocity dispersion and a high ionisation state, which is largely consistent with being ionised by slow shocks. We interpret this feature as either the superposition of the disk and an extraplanar layer of DIG, or as a bar-driven inflow of shocked gas. A double Gaussian component fit to the Hα line also reveals the presence of a nuclear biconic structure whose axis of symmetry is perpendicular to the bar. The two cones (20″ ≃ 500 pc in size) appear blue- and redshifted along the line of sight. The cones stand out for having an Hα emission separated by up to 200 km s−1 from that of the disk, and a high velocity dispersion ∼80–200 km s−1. At the far end of the cones, we observe that the gas is consistent with being ionised by shocks. These features had never been observed before in M83; we postulate that they are tracing a starburst-driven outflow shocking into the surrounding ISM. Finally, we obtain fDIG ∼ 13% in our field of view, and observe that the DIG contribution varies radially between 0.8 and 46%, peaking in the interarm region. We inspect the emission of the H II regions and DIG in ‘BPT’ diagrams, finding that in H II regions photoionisation accounts for 99.8% of the Hα flux, whereas the DIG has a mixed contribution from photoionisation (94.9%) and shocks (5.1%).

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)0.000
metaresearch head score (Gemma)0.000
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.009
GPT teacher head0.182
Teacher spread0.173 · 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

Citations22
Published2022
Admission routes2
Has abstractyes

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