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Hints of quasi-molecular states in 13B via the study of 9Li-4He elastic scattering

2022· article· en· W4283213045 on OpenAlexaff
A. Di Pietro, A. C. Shotter, J. P. Fernández-García, П. Фігуера, M. Fisichella, A. M. Moro, M. Alcorta, M. J. G. Borge, T. Davinson, F. Ferrera, A. M. Laird, М. Латтуада, N. Soić, O. Tengblad, D. Torresi, М. Задро

Bibliographic record

VenuePhysics Letters B · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsTRIUMF
FundersJunta de AndalucíaInstituto Nazionale di Fisica NucleareConsejería de Economía, Conocimiento, Empresas y Universidad, Junta de AndalucíaConsejería de Economía, Innovación, Ciencia y Empleo, Junta de AndalucíaMinisterio de Ciencia, Innovación y Universidades
KeywordsPhysicsExcitationAtomic physicsExcitation functionIsotopeRange (aeronautics)InverseNeutronScatteringElastic scatteringInverse kinematicsNuclear reactionNuclear physicsKinematicsOpticsClassical mechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

This paper reports on elastic scattering excitation functions for the reaction 9Li+4He measured at backward angles in the centre of mass energy range 5 MeV ≤ E≤c.m.9.5 MeV, with the aim of investigating the possible existence of molecular resonances which have been predicted to exist in the case of neutron-rich B-isotopes. Due to the short lifetime of 9Li, the experiment necessitated the use of inverse kinematics on a gaseous 4He target. The Thick Target Inverse Kinematics technique was used which allowed for the measurement of the full excitation function in a single 9Li run. Broad resonances were observed in the excitation region for 13B 15 MeV ≤ Ex≤20 MeV. To understand the nature of such broad structures, various theoretical attempts are reported concerning possible reaction mechanisms for this neutron rich reaction. The most promising approach to interpret the data is within the orbiting reaction scenario.

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 score0.417
Threshold uncertainty score0.527

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 science0.0000.001
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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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
Published2022
Admission routes1
Has abstractyes

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