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Record W267382910 · doi:10.1017/s0074180900213181

CFHT Fabry-Perot 2D spectroscopy in Hα of the ejected Wolf-Rayet ring nebula M1-67: universal multifractal analysis and turbulent status

2003· article· en· W267382910 on OpenAlexaff
Y. Grosdidier, A. F. J. Moffat, Sébastien Blais-Ouellette, Gilles Joncas, A. Acker

Bibliographic record

VenueSymposium - International Astronomical Union · 2003
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversité LavalUniversité de MontréalMcGill University
Fundersnot available
KeywordsPhysicsAstrophysicsScalingTurbulenceIntermittencyMultifractal systemWolf–Rayet starGeometryStarsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Using CFHT-sis Fabry-Perot interferograms of the Wolf-Rayet ring nebula M1-67, we present an investigation of the statistical properties of fluctuating gas motions using structure functions (SFs) traced by Hα emission-line centroid velocities. We consider the SFs 〈|Δ v ( r )| p 〉 of order p, i.e., the spatially averaged moments of order p of the spatial velocity increments at projected spatial scale r of M1-67's velocity field: we test for ( i ) SF scaling, 〈|Δ v ( r )| p 〉 ∝ r ζ( p ) , and ( ii ) nonlinearity of the observed scaling exponents ζ( p )s, as expected for intermittent flows. We find that there is a clear correlation at scales 0.02-0.22 pc between the mean quadratic differences of radial velocities and distance over the surface of M1-67. The first and second order SFs are found to scale as 〈|Δ v ( r )|〉 ∝ r 0.5 and 〈|Δ v ( r )| 2 〉 ∝ r 0.9 (Grosdidier et al. 2001). The former scaling law strongly suggests that supersonic turbulence is at play in M1-67, on the other hand, the latter scaling law agrees very well with Larson-type laws for velocity turbulence. Additionally, we can discuss the nature of the turbulence in terms of Universal Multifractals (UM), a continuous-scale limit of multiplicative cascades (Schertzer & Lovejoy 1987) and derive the level of intermittency in the nebula.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

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

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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2003
Admission routes1
Has abstractyes

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