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Nucleosynthesis in neutron-rich ejecta from quark-novae

2007· article· en· W3098273479 on OpenAlexafffund
Prashanth Jaikumar, B. S. Meyer, Kaori Otsuki, Rachid Ouyed

Bibliographic record

VenueSpringer Link (Chiba Institute of Technology) · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsUniversity of Calgary
FundersNuclear PhysicsNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space AdministrationU.S. Department of EnergyNational Science Foundation
KeywordsNucleosynthesisPhysicsr-processEjectaNeutron starAstrophysicsSupernovaNuclear physicsKilonovaQuark starStrange matter

Abstract

fetched live from OpenAlex

We explore heavy-element nucleosynthesis by rapid neutron capture (r-process) in the decompressing ejecta from the surface of a neutron star. The decompression is triggered by a violent phase transition to strange quark matter (quark-nova scenario). The presence of neutron-rich large Z nuclei (40,95) < $(Z,A)$ < (70,177), the large neutron-to-seed ratio, and the low electron fraction Ye ~ 0.03 in the decompressing ejecta present favorable conditions for the r-process. We perform network calculations that are adapted to the quark-nova conditions, and which mimic usual $(n-\gamma)$ equilibrium r-process calculations during the initially cold decompression phase. They match to dynamical r-process calculations at densities below neutron drip (4 $\times$ 1011 g cm-3). We present results for the final element abundance distribution with and without heating from nuclear reactions, and compare to the solar abundance pattern of r-process elements. We highlight the distinguishing features of quark-novae by contrasting it with conventional nucleosynthetic sites such as type II supernovae and neutron star mergers, especially in the context of heavy-element compositions of extremely metal-deficient stars.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.286
Teacher spread0.273 · 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

Citations29
Published2007
Admission routes2
Has abstractyes

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