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Record W4390969926 · doi:10.3847/2041-8213/ad0fe1

Magnetized Outflows from Short-lived Neutron Star Merger Remnants Can Produce a Blue Kilonova

2024· article· en· W4390969926 on OpenAlexafffund
Sanjana Curtis, P. Bosch, Philipp Mösta, David Radice, Sebastiano Bernuzzi, Albino Perego, Roland Haas, Erik Schnetter

Bibliographic record

VenueThe Astrophysical Journal Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersNuclear PhysicsOffice of ScienceInstitut Périmètre de physique théoriqueMinistry of Colleges and UniversitiesNederlandse Organisatie voor Wetenschappelijk OnderzoekNational Science FoundationGovernment of CanadaDeutsche ForschungsgemeinschaftU.S. Department of Energy
KeywordsKilonovaPhysicsNeutron starEjectaAstrophysicsNucleosynthesisr-processNeutrinoAstronomyGamma-ray burstNuclear physicsSupernova

Abstract

fetched live from OpenAlex

Abstract We present a 3D general-relativistic magnetohydrodynamic simulation of a short-lived neutron star remnant formed in the aftermath of a binary neutron star merger. The simulation uses an M1 neutrino transport scheme to track neutrino–matter interactions and is well suited to studying the resulting nucleosynthesis and kilonova emission. A magnetized wind is driven from the remnant and ejects neutron-rich material at a quasi-steady-state rate of 0.8 × 10−1 M ⊙s−1. We find that the ejecta in our simulations underproduce r-process abundances beyond the second r-process peak. For sufficiently long-lived remnants, these outflows alone can produce blue kilonovae, including the blue kilonova component observed for AT2017gfo.

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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.295
Teacher spread0.279 · 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

Citations24
Published2024
Admission routes2
Has abstractyes

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