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Role of Multichance Fission in the Description of Fission-Fragment Mass Distributions at High Energies

2017· article· en· W2770084988 on OpenAlexaff
K. Hirose, K. Nishio, S. Tanaka, R. Léguillon, H. Makii, I. Nishinaka, R. Orlandi, K. Tsukada, J. Smallcombe, M.J. Vermeulen, Satoshi Chiba, Y. Aritomo, T. Ohtsuki, Keita Nakano, Shouhei Araki, Yutaka Watanabe, Ryotaro Tatsuzawa, Naoyuki Takaki, N. Tamura, Shin Goto, I. Tsekhanovich, A. N. Andreyev

Bibliographic record

VenuePhysical Review Letters · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsTRIUMF
FundersScience and Technology Facilities CouncilInternational Atomic Energy AgencyMinistry of Education, Culture, Sports, Science and TechnologyKindai University
KeywordsFissionPhysicsNuclideCluster decayNuclear physicsAtomic physicsCold fissionMass distributionRange (aeronautics)ExcitationMass numberFission productsNuclear fissionNeutron emissionMaterials scienceNeutron

Abstract

fetched live from OpenAlex

Fission-fragment mass distributions were measured for ^{237-240}U, ^{239-242}Np, and ^{241-244}Pu populated in the excitation-energy range from 10 to 60 MeV by multinucleon transfer channels in the reaction ^{18}O+^{238}U at the Japan Atomic Energy Agency tandem facility. Among them, the data for ^{240}U and ^{240,241,242}Np were observed for the first time. It was found that the mass distributions for all the studied nuclides maintain a double-humped shape up to the highest measured energy in contrast to expectations of predominantly symmetric fission due to the washing out of nuclear shell effects. From a comparison with the dynamical calculation based on the fluctuation-dissipation model, this behavior of the mass distributions was unambiguously attributed to the effect of multichance fission.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.317
Teacher spread0.294 · 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 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

Citations73
Published2017
Admission routes1
Has abstractyes

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