Development and Implementation of the D-Pace Ion Source Automation System for the VITA Accelerator
Bibliographic record
Abstract
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%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".