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Development and Implementation of the D-Pace Ion Source Automation System for the VITA Accelerator

2024· article· en· W4399638921 on OpenAlexaboutno aff
Alexey Koshkarev, Ivan Shchudlo, S. Yu. Taskaev

Bibliographic record

VenueSiberian Journal of Physics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAutomationIon sourceNeutron sourceNuclear physicsNeutronNuclear engineeringPhysicsComputer scienceEngineeringMechanical engineeringPlasma

Abstract

fetched live from OpenAlex

The VITA accelerator neutron source based on a vacuum insulated tandem accelerator operates at the Institute of Nuclear Physics SB RAS. The development of a separate compact facility for the generation of fast neutrons is an actual task, it will allow the treatment of malignant tumors via boron-neutron capture therapy with fast neutrons and a number of other applications. To control the facility, store and analyze data, the author has previously created an automation system that allows the operator to provide long-term stable proton or deuteron beam production in a wide range of energy and current variations, and scientific staff to obtain experimental data and process them in real time. Currently, the BINP SB RAS is manufacturing the accelerating neutron source VITA for the National Medical Research Center for Oncology named after N. N. Blokhina in Moscow. It is planned to put it into operation in 2025. In contrast to the operating experimental installation of the BINP SB RAS, an ion source from the D-Pace company (Canada) will be used. This paper presents the automation procedure for the new ion source, control algorithms, PID controller coefficients and operating parameters, which made it possible to obtain a maximum average beam current on the Faraday cup of 13 mA with a stability of 0.14%.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.161

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 head0.012
GPT teacher head0.270
Teacher spread0.259 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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