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Record W3200422088 · doi:10.1109/tasc.2021.3111396

High Spatial Resolution Neutron Transmission Imaging Using a Superconducting Two-Dimensional Detector

2021· article· en· W3200422088 on OpenAlexaff
Hiroaki Shishido, Kazuma Nishimura, The Dang Vu, Kazuya Aizawa, Kenji Kojima, Tomio Koyama, Kenichi Oikawa, Masahide Harada, Takayuki Oku, Kazuhiko Soyama, Shigeyuki Miyajima, Mutsuo Hidaka, S. Suzuki, M. Tanaka, Shuichi Kawamata, Takekazu Ishida

Bibliographic record

VenueIEEE Transactions on Applied Superconductivity · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of British ColumbiaTRIUMF
FundersJapan Society for the Promotion of Science
KeywordsNeutron imagingNeutronNeutron detectionMaterials scienceNeutron sourceNeutron captureOpticsBonner sphereDetectorNeutron temperatureNeutron stimulated emission computed tomographyNuclear magnetic resonanceNeutron cross sectionNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Neutron imaging is one of the most powerful tools for nondestructive inspection owing to the unique characteristics of neutron beams, such as high permeability for many heavy metals, high sensitivity for certain light elements, and isotope selectivity owing to a specific nuclear reaction between an isotope and neutrons. In this study, we employed a superconducting detector, current-biased kinetic-inductance detector (CB-KID) for neutron imaging using a pulsed neutron source. We employed the delay-line method, and high spatial resolution imaging with only four reading channels was achieved. We also performed wavelength-resolved neutron imaging by the time-of-flight method for the pulsed neutron source. We obtained the neutron transmission images of a Gd–Al alloy sample, inside which single crystals of GdAl <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_3$</tex-math></inline-formula> were grown, using the delay-line CB-KID. Single crystals were well imaged, in both shapes and distributions, throughout the Al–Gd alloy. We identified Gd nuclei via neutron transmissions that exhibited characteristic suppression above the neutron wavelength of 0.03 nm. In addition, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{155}$</tex-math></inline-formula> Gd resonance dip, a dip structure of the transmission caused by the nuclear reaction between an isotope and neutrons, was observed even when the number of events was summed over a limited area of 15 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> m × 12 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> m. Gd selective imaging was performed using the resonance dip of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{155}$</tex-math></inline-formula> Gd, and it showed clear Gd distribution even with a limited neutron wavelength range of 1 pm.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.331
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.252
Teacher spread0.232 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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