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Record W2981451776 · doi:10.1093/mnras/stz2938

Flybys in protoplanetary discs – II. Observational signatures

2019· article· en· W2981451776 on OpenAlexaff
Nicolás Cuello, F. Louvet, Daniel Mentiplay, C. Pinte, Daniel J. Price, Andrew J. Winter, Rebecca Nealon, F. Ménard, Giuseppe Lodato, Giovanni Dipierro, Valentin Christiaens, Matías Montesinos, Jorge Cuadra, Guillaume Laibe, Lucas A. Cieza, Ruobing Dong, Richard D. Alexander

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Victoria
FundersAustralian Research CouncilEuropean Research CouncilHorizon 2020 Framework ProgrammeConsejo Nacional de Innovación, Ciencia y TecnologíaChinese Academy of SciencesFondo Nacional de Desarrollo Científico y TecnológicoAgence Nationale de la Recherche
KeywordsPhysicsProtostarRadiative transferAstrophysicsMillimeterAstronomyAccretion (finance)InfraredSerpensStar formationStarsOptics

Abstract

fetched live from OpenAlex

ABSTRACT Tidal encounters in star clusters perturb discs around young protostars. In Cuello et al., we detailed the dynamical signatures of a stellar flyby in both gas and dust. Flybys produce warped discs, spirals with evolving pitch angles, increasing accretion rates, and disc truncation. Here, we present the corresponding observational signatures of these features in optical/near-infrared scattered light and (sub) millimetre continuum and CO line emission. Using representative prograde and retrograde encounters for direct comparison, we post-process hydrodynamical simulations with radiative transfer methods to generate a catalogue of multiwavelength observations. This provides a reference to identify flybys in recent near-infrared and submillimetre observations (e.g. RW Aur, AS 205, HV Tau and DO Tau, FU Ori, V2775 Ori, and Z CMa).

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.401

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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 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

Citations77
Published2019
Admission routes1
Has abstractyes

Explore more

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