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Neutron skin and signature of the N = 14 shell gap found from measured proton radii of 17−22N

2019· article· en· W2913545714 on OpenAlexafffund
S. Bagchi, R. Kanungo, W. Horiuchi, G. Hagen, Titus Morris, S. R. Stroberg, Toshio Suzuki, F. Ameil, J. Atkinson, Y. Ayyad, D. Cortina‐Gil, I. Dillmann, A. Estradé, A. Evdokimov, F. Farinon, H. Geißel, G. Guastalla, R. Janik, S. Kaur, R. Knöbel, J. Kurcewicz, Yu. A. Litvinov, M. Marta, I. Mukha, C. Nociforo, H. J. Ong, S. Piétri, A. Prochazka, C. Scheidenberger, B. Sitár, P. Strmeň, M. Takechi, Junki Tanaka, Y. Tanaka, I. Tanihata, S. Terashima, J. E. Ramirez Vargas, H. Weick, J. S. Winfield

Bibliographic record

VenuePhysics Letters B · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsDalhousie UniversityTRIUMFSaint Mary's University
FundersOak Ridge National LaboratoryNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaJapan Society for the Promotion of ScienceOffice of ScienceNuclear PhysicsBeihang UniversityTRIUMFU.S. Department of Energy
KeywordsPhysicsNeutronProtonNucleonNuclear physicsHaloIsotopeShell (structure)Nuclear drip lineAtomic physics

Abstract

fetched live from OpenAlex

A thick neutron skin emerges from the first determination of root mean square radii of the proton distributions for 17-22 N from charge changing cross section measurements around 900 A MeV at GSI. Neutron halo effects are signalled for 22 N from an increase in the proton and matter radii. The radii suggest an unconventional shell gap at N = 14 arising from the attractive proton-neutron tensor interaction, in good agreement with shell model calculations. Ab initio, in-medium similarity re-normalization group, calculations with a state-of-the-art chiral nucleon-nucleon and three-nucleon interaction reproduce well the data approaching the neutron drip-line isotopes but are challenged in explaining the complete isotopic trend of the radii.

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 score0.191
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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

Citations51
Published2019
Admission routes2
Has abstractyes

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