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Record W2902377709 · doi:10.3847/2041-8213/ab01e4

The Signature of a Windy Radio Supernova Progenitor in a Binary System

2019· article· en· W2902377709 on OpenAlexaff
Almog Yalinewich, Simon Portegies Zwart

Bibliographic record

VenueThe Astrophysical Journal Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsCanadian Institute for Theoretical Astrophysics
Fundersnot available
KeywordsPhysicsSupernovaAstrophysicsBinary numberEccentricity (behavior)Orbital decayEjectaMass ratioAstronomySatelliteLaw

Abstract

fetched live from OpenAlex

Abstract Type II supernova progenitors are expected to emit copious amounts of mass in a dense stellar wind prior to the explosion. When the progenitor is a member of a binary, the orbital motion modulates the density of this wind. When the progenitor explodes, the high-velocity ejecta collides with the modulated wind, which in turn produces a modulated radio signal. In this Letter we derive general analytic relations between the parameters of the radio signal modulations and binary parameter in the limit of large member-mass ratio. We use these relations to infer the semimajor axis of SN 1979c and a lower bound for the mass of the companion. We further constrain the analytic estimates by numerical simulations using the Astrophysical Multipurpose Software Environment framework. In these calculations we simulate the progenitor binary system including the wind and the gravitational effect of a companion star. The simulation output is compared to the observed radio signal in supernova SN 1979C. We find that it must have been a binary with an orbital period of about 2000 yr. If the exploding star evolved from a ∼18 M ⊙ zero-age main sequence at solar metallicity, we derive a companion mass of 5–12 M ⊙ in an orbit with an eccentricity lower than about 0.8.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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 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

Citations3
Published2019
Admission routes1
Has abstractyes

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