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Record W4308980531 · doi:10.1063/5.0101814

Neutron emission spectrometer to measure ion temperature on the Fusion Demonstration Plant

2022· article· en· W4308980531 on OpenAlexaff
Patrick Carle, F. Retière, A. Sher, R. Underwood, K. Starosta, M. Hildebrand, Sandra Barsky, Stephen Howard

Bibliographic record

VenueReview of Scientific Instruments · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsSimon Fraser UniversityTRIUMFGeneral Fusion (Canada)
Fundersnot available
KeywordsNeutronSpectrometerScintillatorMaterials scienceNeutron emissionPhysicsNeutron detectionTime of flightNeutron sourceNuclear physicsNeutron temperatureOpticsDetector

Abstract

fetched live from OpenAlex

General Fusion is building the Fusion Demonstration Plant to demonstrate a magnetized target fusion scheme in which a deuterium plasma is heated from 200 eV to 10 keV by piston-driven compression of a liquid-lithium liner. The multilayer coaxial time-of-flight neutron emission spectrometer is designed to measure the ion temperature near peak compression at which time the neutron yield will approach 1018 neutrons/s. The neutron energy distribution is expected to be Gaussian since the machine uses no neutral beam or radio-frequency heating. In this case, analysis shows that as few as 500 coincidence events should be sufficient to accurately measure the ion temperature. This enables a fast time resolution of 10 µs, which is required to track the rapid change in temperature approaching peak compression. We overcome the challenges of neutron pile-up and event ambiguity with a compact design having two layers of segmented scintillators. The error in the ion temperature measurement is computed as a function of the neutron spectrometer’s geometric parameters and used to optimize the design for the case of reaching 10 keV at peak compression.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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