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Energy-Resolved Neutron Imaging using a Delay Line Current-Biased Kinetic-Inductance Detector

2020· article· en· W3046817275 on OpenAlexaff
Hiroaki Shishido, Kazuma Nishimura, The Dang Vu, Kenji Kojima, Tomio Koyama, Kenichi Oikawa, Masahide Harada, Shigeyuki Miyajima, Mutsuo Hidaka, Takayuki Oku, Kazuhiko Soyama, Kazuya Aizawa, S. Suzuki, Takekazu Ishida

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of British ColumbiaTRIUMF
Fundersnot available
KeywordsPhysicsMeander (mathematics)NeutronDetectorOpticsKinetic inductanceKinetic energyInductanceNeutron detectionMaterials scienceVoltageNuclear physicsGeometry

Abstract

fetched live from OpenAlex

Abstract We demonstrate the development of an energy resolved neutron transmission imaging system via a solid-state superconducting detector, called current-biased kinetic-inductance detector (CB-KID). CB-KIDs comprise X and Y superconducting Nb meanderlines with Nb ground plane and a 10 B conversion layer, which converts a neutron to two charged particles. High-energy charged particles are able to create quasi-particle hot spots simultaneously in the X and Y meander lines, and thus, the local Cooper pair density in meander lines is reduced temporary. When DC-bias currents are fed into the meander lines, double pairs of voltage pulses are generated at the hot spots and propagate toward both ends of the meander lines as electromagnetic waves. The position of the original hot spot is determined by a difference in arrival times of the two pulses at the two ends for X and Y meander lines, independently. This is so-called the delay-line method, and allows us to reconstruct the two-dimensional neutron transmission image of a test sample with four signal readout lines. We examined the capability of high spatial and energy (wavelength) resolved neutron transmission imaging over the sensor active area of 15 × 15 mm 2 for various samples, including biological and metal ones. We also demonstrated the capability for the Bragg edge transmission and an energy-resolved neutron image in which stainless-steel specimens were discriminating from other specimens.

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: none
Teacher disagreement score0.614
Threshold uncertainty score0.827

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 head0.038
GPT teacher head0.270
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.

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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