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Record W4387299851 · doi:10.1117/12.2682610

Fast neutron radiography-based on single-photon digital photosensor: concept and demonstration

2023· article· en· W4387299851 on OpenAlexaff
J.‐F. Pratte, K. Deslandes, T. Rossignol, N. Roy, F. Vachon, Robin Scarpellini, Guillaume Théberge‐Dupuis, Paul Hausladen, L. Fabris, Serge A. Charlebois

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPhotonNeutronPhotodetectorNeutron imagingOpticsPhysicsComputer scienceRadiographyNuclear physicsMedical physics

Abstract

fetched live from OpenAlex

For decades, fast neutron radiography performed using pulse-counting detectors employed PMTs coupled to plastic scintillator pixel arrays. SiPM-based systems are now sought to replace fragile PMTs, but conventional, “analog” SiPMs suffer from intrinsic limitations which limit their achievable performance. Among these limitations, a complete analog readout and digitizer chain is required, a counterintuitive approach when considering that the single-photon avalanche diode (SPAD), the basic unit cell of SiPMs, is a Boolean detector providing digital detection at the sensor level. This paper outlines a new concept for neutron radiography instrumentation by using photon-to-digital converters (PDCs, aka digital SiPMs), a fully digital solution to sense the scintillation light.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.430

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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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