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Record W2050449957 · doi:10.1118/1.3244157

Poster - Wed Eve-53: Characteristics of Radiation Induced Light in Optical Fibers

2009· article· en· W2050449957 on OpenAlexaffabout
I Silva, Geordi Pang

Bibliographic record

VenueMedical Physics · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsCherenkov radiationOpticsRadiationDetectorOptical fiberOptoelectronicsPhysicsParticle detectorCherenkov detectorMaterials science

Abstract

fetched live from OpenAlex

Most electronic portal imaging devices (EPIDs) developed so far use a Cu plate/phosphor screen to absorb x rays. This approach uses a thin screen (∼ 2 mm) to obtain a high spatial resolution, however this results in a low quantum efficiency (QE) for megavoltage (MV) x rays (typically 2–4%). The phosphor screen also contains high atomic number (high-Z) materials, resulting in an over-response of the detector to low energy x rays in dosimetric verification. Our overall goal is to develop a new high QE MV x-ray detector made of a low-Z material for both geometric and dosimetric verification in radiotherapy. The approach is based on converting x-ray energy into Cherenkov radiation in optical fibers. The design uses a thick (∼ 10–30 cm) matrix of optical fibres (to dramatically improve the QE) aligned with the incident x rays. This work investigates the characteristics of radiation-induced light in optical fibers and demonstrates that the predominant light source in optical fibers under high energy irradiation is indeed Cherenkov radiation. Thus, the feasibility of using Cherenkov radiation as the primary light source in the proposed Cherenkov detector is validated. This work was supported by the National Sciences and Engineering Research Council of Canada (NSERC).

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.380

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 head0.009
GPT teacher head0.247
Teacher spread0.237 · 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 designOther design
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
Published2009
Admission routes2
Has abstractyes

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