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Record W4415594457 · doi:10.1109/tns.2025.3625255

Enhancing Performance of Optical Fiber-Based Sensor for Proton Dosimetry Through Pre-Irradiation Treatment

2025· article· W4415594457 on OpenAlexaff
Fiammetta Fricano, Adriana Morana, Cornelia Hoehr, Cosimo Campanella, C. Bélanger-Champagne, M. Trinczek, Gilles Mélin, Thierry Robin, Damien Lambert, A. Boukenter, Emmanuel Marin, Y. Ouerdane, Philippe Paillet, Sylvain Girard

Bibliographic record

VenueIEEE Transactions on Nuclear Science · 2025
Typearticle
Language
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsTRIUMF
Fundersnot available
KeywordsDosimetryOptical fiberCalibrationProtonSensitivity (control systems)Multi-mode optical fiberRadiationBragg peakAbsorbed dose

Abstract

fetched live from OpenAlex

We compare nitrogen-doped radioluminescent multimode optical fibers in their pristine and pre-irradiated states under proton irradiation. The results show that pre-irradiation significantly enhances fiber sensitivity and radioluminescence, leading to improved Bragg Peak and Spread Out Bragg Peak reproduction. The response shows minimal energy dependence (<5%), enabling single calibration across 26–63 MeV. This makes the technology suitable for accurate, real-time proton flux and dose monitoring, with promising applications not only in medical dosimetry but also in radiation testing for space technologies. Geant4 simulations further support these findings by highlighting how geometric differences between the fiber and the Markus chamber affect the dose deposition and response curves.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.263
Teacher spread0.249 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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