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Kinetic inductance neutron detector operated at near critical temperature

2020· article· en· W3046784548 on OpenAlexaff
The Dang Vu, Kazuma Nishimura, Hiroaki Shishido, Masahide Harada, Kenichi Oikawa, Shigeyuki Miyajima, Mutsuo Hidaka, Takayuki Oku, Kazuhiko Soyama, Kazuya Aizawa, Kenji Kojima, Tomio Koyama, Alex Malins, Masahiko Machida, Takekazu Ishida

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsTRIUMF
Fundersnot available
KeywordsDetectorNeutronNuclear physicsNeutron detectionKinetic inductancePhysicsElectronKinetic energyMonte Carlo methodNeutron temperaturePhotonMaterials scienceComputational physicsOpticsInductanceVoltage

Abstract

fetched live from OpenAlex

Abstract We previously succeeded in constructing and demonstrating the capability of a neutron imaging system based on a superconducting current-biased kinetic inductance detector (CB-KID). In the present work, we systematically studied the characteristics of the superconducting neutron detector to improve the spatial resolution and detection efficiency. We found that the number of neutron detection events with CB-KID remarkably increased when the detector temperature increased from 4 K to the critical temperature T c . We observed systematic changes of neutron signals as a function of the detector temperature from 4 K to T c . We evaluated the detection efficiency of the CB-KID detector, and compared with PHITS Monte Carlo simulations, which modeled the sequential physical processes for the 10 B(n,α) 7 Li reaction, the transport dynamics, and the energy deposition by particles including neutrons, 4 He particles, 7 Li particles, photons, and electrons.

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 score0.265
Threshold uncertainty score0.629

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 science0.0000.000
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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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