MétaCan
Menu
Back to cohort
Record W2544106122 · doi:10.1109/nssmic.2012.6551598

Analytic response functions in compound semiconductor detectors

2012· article· en· W2544106122 on OpenAlexaff
Seungman Yun, Ho Kyung Kim, Hanbean Youn, Jesse Tanguay, Ian A. Cunningham

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsRobarts Clinical Trials
Fundersnot available
KeywordsMonte Carlo methodPhysicsDetectorPhotonCompton scatteringComputational physicsSemiconductor detectorPhoton energyOpticsPhotoelectric effectMathematicsStatistics

Abstract

fetched live from OpenAlex

The absorbed energy distribution (A ED) in x-ray imaging detectors is an important factor that limits both energy resolution and image quality. In the diagnostic energy range (10120 ke V), escape of characteristic photons following photoelectric absorption and Compton scatter photons are primary sources of absorbed-energy dispersion in x-ray detectors. In this article, we describe the development of an analytic model of the AED of compound semiconductor detectors including the effects of escape and reabsorption of characteristic and Compton-scatter photons. We derive analytic expressions for semi-infinite slab geometry and validate our approach by Monte Carlo simulations using the commercial MCNPSTM(ORNL, USA) code. In all cases, there was good agreement between analytic and Monte Carlo calculations. The analytic model allows us to obtain the 2D x-ray response matrix of arbitrary compound materials without time-consuming Monte Carlo simulations. We believe this model will be useful for correcting spectral distortion artifacts commonly observed in photon-counting applications and optimal design and development of novel x-ray detectors.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.229
Teacher spread0.217 · 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 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
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced X-ray and CT ImagingFrench-language works237,207