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Depth-Dose Distribution Dependence on the Energy Profile of Linear and Laser Wakefield Accelerator Electron Beams

2021· article· en· W3203631523 on OpenAlexaff
Tuấn Anh Nguyễn, C. Rangacharyulu, Châu Văn Tạo

Bibliographic record

VenueJACOW · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotocathodes and Microchannel Plates
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLinear particle acceleratorElectronPhysicsLaserDistribution (mathematics)Energy distributionNuclear physicsEnergy (signal processing)Plasma accelerationAtomic physicsComputational physicsOpticsBeam (structure)MathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

The depth-dose distributions of 10 MeV electron beams used for food irradiation and sterilization purposes at Research and Development Center for Radiation Technology, HCMC, Vietnam are measured and the results are well reproduced by the MCNP simulations. We extend the simulations to predict the dose depth distribution for 10 MeV electron beams with the energy profiles of a model Laser Wake Field accelerator (LWFA). The dosimetry and simulation results show that the maximum dose of the depth-dose curve inside the product is 1.4 times surface dose with an area density limit of 8.6 g/cm² for two-sided irradiation with nearly mono-energetic beams from the linear accelerator and the corresponding parameters for LWFA are 1.2 times surface dose and 13.0 g/cm², respectively.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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
Published2021
Admission routes1
Has abstractyes

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