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Record W2095291908 · doi:10.1017/s1743921314012472

The cool and warm molecular gas in M82 with <i>Herschel</i>-SPIRE

2012· article· en· W2095291908 on OpenAlexaff
J. Kamenetzky, J. Glenn, Naseem Rangwala, Philip R. Maloney, M. Bradford, C. D. Wilson, G. J. Bendo, M. Baes, A. Boselli, A. Cooray, K. G. Isaak, V. Lebouteiller, S. C. Madden, P. Panuzzo, M. R. P. Schirm, L. Spinoglio, R. Wu

Bibliographic record

VenueProceedings of the International Astronomical Union · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsMcMaster University
FundersScience and Technology Facilities Council
KeywordsSpire (mollusc)AstrophysicsPhysicsGalaxyLine (geometry)Trace gasTurbulenceStar formationHydrogenAtmospheric sciencesThermodynamics

Abstract

fetched live from OpenAlex

Abstract We present Herschel-SPIRE imaging spectroscopy (194-671 μm) of the bright starburst galaxy M82. We use RADEX and a Bayesian Likelihood Analysis to simultaneously model the temperature, density, column density, and filling factor of both the cool and warm components of molecular gas traced by the entire CO ladder up to J=13-12. The high-J lines observed by SPIRE trace much warmer gas (~500 K) than those observable from the ground. The addition of 13CO (and [C I]) is new and indicates that [C I] may be tracing different gas than 12CO. At such a high temperature, cooling is dominated by molecular hydrogen; we conclude with a discussion on the possible excitation processes in this warm component. Photon-dominated region (PDR) models require significantly higher densities than those indicated by our Bayesian likelihood analysis in order to explain the high-J CO line ratios, though cosmic-ray enhanced PDR models can do a better job reproducing the emission at lower densities. Shocks and turbulent heating are likely required to explain the bright high-J 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 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)0.001
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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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