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Latest results of Skyrme-Hartree-Fock-Bogoliubov mass formulas

2016· article· en· W2279746179 on OpenAlexaff
S. Goriely, N. Chamel, John Pearson

Bibliographic record

VenueJournal of Physics Conference Series · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPhysicsNeutron starNeutronHartree–Fock methodSupernovaNucleosynthesisEquation of stateNuclear physicsNuclear matterMean field theoryParticle physicsNucleonAstrophysicsQuantum mechanics

Abstract

fetched live from OpenAlex

The latest developments made in deriving accurate mass predictions within the microscopic Skyrme Hartree-Fock-Bogolyubov (HFB) approach are described. Using the HFB method, we have refitted our BSk20 and BSk21 models, on which were based the HFB-20 and HFB-21 mass tables, to the 2353 measured masses of nuclei with N and Z ≥ 8 given in the newly available 2012 Atomic Mass Evaluation. In addition, we have now investigated the sensitivity to the symmetry coefficient J and the stiffness of the neutron matter equation of state. We present here in all 5 new Skyrme interactions, along with their corresponding mass tables. The root-mean square deviation obtained with our preferred model (HFB-24) with respect to essentially all the 2353 available mass data falls to 0.549 MeV, the best value ever found within the mean-field framework. Since our Skyrme force is also constrained by the properties of pure neutron matter this new model is particularly well-suited for application to astrophysical problems involving a neutron-rich environment, such as the elucidation of the r-process of nucleosynthesis, and the description of supernova cores and neutron-star crusts.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.277
Teacher spread0.251 · 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 designBench or experimental
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

Citations9
Published2016
Admission routes1
Has abstractyes

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