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Record W4390604054 · doi:10.1109/tns.2024.3350342

Mixed-Field Radiation Monitoring and Beam Characterization Through Silicon Diode Detectors

2024· article· en· W4390604054 on OpenAlexafffund
Kacper Biłko, Rubén García Alía, Mario Sacristán Barbero, Sylvain Girard, Ygor Quadros de Aguiar, Matteo Cecchetto, C. Bélanger-Champagne, Salvatore Danzeca, Wojtek Hajdas, Alex Hands, Pedro Martín‐Holgado, Yolanda Morilla, Daniel Prelipcean, Federico Ravotti, Marc Sebban

Bibliographic record

VenueIEEE Transactions on Nuclear Science · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsTRIUMF
FundersEuropean CommissionTRIUMF
KeywordsLarge Hadron ColliderDetectorPhysicsRadiationDiodeBeam (structure)Particle detectorOpticsRadiation hardeningCalibrationRadiation damageSiliconOptoelectronicsNuclear physics

Abstract

fetched live from OpenAlex

We present a calibration of a commercial silicon diode with proton and alpha beams and gamma rays. The diode together with a fast acquisition chain can be exploited for both direct and indirect (through the secondary radiation field) beam characterization. Within this work, we demonstrate the detector capabilities of resolving single-energy-deposition events and independently measuring dose rate and beam flux. Profiting from the mixed radiation field in CERN’s high-energy accelerator mixed field facility (CHARM) we show how the silicon detector can be exploited to characterize the mixed radiation field present in it, which in turn is used for validating the radiation tolerance of components and systems to be installed in the European Organization for Nuclear Research (CERN) accelerator complex.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.272
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes2
Has abstractyes

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