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Record W1973736242 · doi:10.1063/1.4874085

Production of 44Ti in neutrino-driven aspherical supernova explosions

2014· article· en· W1973736242 on OpenAlexaff
Shin Ichiro Fujimoto, M. Ono, Masa Aki Hashimoto, Kei Kotake

Bibliographic record

VenueAIP conference proceedings · 2014
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsInstitute of Particle Physics
FundersJapan Society for the Promotion of Science
KeywordsSupernovaPhysicsEjectaNeutrinoNucleosynthesisExplosive materialRotation (mathematics)Nuclear physicsAstrophysicsChemistryComputer science

Abstract

fetched live from OpenAlex

We examine the synthesis of 44Ti in a neutrino-driven aspherical supernova (SN), focusing on reaction rates related to 44Ti and rotation of a progenitor. We have performed 2D hydrodynamic simulations of SN of a 15M⊙ progenitor, whose angular velocity is manually set to be a cylindrical distribution and have followed explosive nucleosynthesis in the ejecta. We find that the faster rates of 40Ca(α,γ)44Ti and the slower rate of 44Ti(α,p)47V lead to more massive ejection of 44Ti and 56Ni and larger ratios <44Ti/56Ni>. Faster rotation also results in more massive ejection of 44Ti and 56Ni. Ratios <44Ti/56Ni> are however independent from rotation. Large masses of 44Ti and large ratios observed in SN 1987A and Cas A (> 1O−4M⊙ and 1-2 respectively) are not realized in all the models.

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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.023
GPT teacher head0.229
Teacher spread0.206 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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