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Record W2787688820 · doi:10.3847/1538-4357/aaaf65

A UV-to-NIR Study of Molecular Gas in the Dust Cavity around RY Lupi

2018· article· en· W2787688820 on OpenAlexaff
Nicole Arulanantham, Kevin France, Keri Hoadley, C. F. Manara, P. C. Schneider, J. M. Alcalá, Andrea Banzatti, Hans Moritz Günther, A. Miotello, Nienke van der Marel, E. F. van Dishoeck, Catherine Walsh, Jonathan P. Williams

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsAstrophysicsGaussianPhysicsComponent (thermodynamics)Flux (metallurgy)Line (geometry)Spectral lineEmission spectrumGaussian network modelChemistryGeometryAstronomyMathematics

Abstract

fetched live from OpenAlex

Abstract We present a study of molecular gas in the inner disk around RY Lupi, with spectra from HST-COS, HST-STIS, and VLT-CRIRES. We model the radial distribution of flux from hot gas in a surface layer between r = 0.1–10 au, as traced by Lyα-pumped H2. The result shows H2 emission originating in a ring centered at ∼3 au that declines within r < 0.1 au, which is consistent with the behavior of disks with dust cavities. An analysis of the H2 line shapes shows that a two-component Gaussian profile is statistically preferred to a single-component Gaussian. We interpret this as tentative evidence for gas emitting from radially separated disk regions . The 4.7 μm 12CO emission lines are also well fit by two-component profiles . We combine these results with 10 μm observations to form a picture of gapped structure within the mm-imaged dust cavity, providing the first such overview of the inner regions of a young disk. The HST SED of RY Lupi is available online for use in modeling efforts.

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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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

Citations19
Published2018
Admission routes1
Has abstractyes

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