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Crystalline Orientation of CaF<sub>2</sub> window determined by Neutron Transmission Imaging using a Delay Line Current-Biased Kinetic-Inductance Detector

2023· article· en· W4385668278 on OpenAlexaff
Hiroaki Shishido, 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

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of British ColumbiaTRIUMF
Fundersnot available
KeywordsNeutronNeutron detectionDetectorNeutron sourceOpticsPhysicsKinetic inductanceNeutron diffractionMaterials scienceNuclear magnetic resonanceNuclear physicsInductanceDiffractionVoltage

Abstract

fetched live from OpenAlex

Abstract The combination of a high speed two-dimensional neutron detector and an intensive pulsed neutron source provides not only neutron transmission imaging but also information on crystal structures, orientations and constituent elements by analyzing neutron transmission spectra. The delay-line current-biased kinetic-inductance detector (CB-KID) is a two-dimensional superconducting neutron detector with high spatial and temporal resolutions, and multi-hit tolerance. We demonstrated that the delay-line CB-KID with a 10 B neutron conversion layer can be applied for neutron transmission measurements up to 100 keV. We observed two-different dip structures in the transmission spectra in a CaF 2 single crystal, i.e., one is from the Bragg diffraction and the other is from nuclear resonance absorption. We consider that some duplicated structures in resonance absorption dips are originating from a double-bunch structure of proton pulses for producing pulsed neutrons in the accelerator.

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 score0.144
Threshold uncertainty score0.823

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 communication0.0000.001
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 head0.021
GPT teacher head0.272
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

Citations0
Published2023
Admission routes1
Has abstractyes

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