Beam Tuning Automation Activities at TRIUMF
Bibliographic record
Abstract
The particle accelerator complex at TRIUMF provides beams for secondary particle production including rare isotopes. The post acceleration of rare isotope ions demands frequent changes of beam properties like energy and changes of the ion species in terms of isotope and charge state. To facilitate these changes to beam properties and species, a High Level Applications (HLA) framework has been developed that provides the essential elements necessary for app development: access to sophisticated envelope simulations and any necessary beamline data, integration with the control system, version control, deployment and issue tracking, and training materials. With this framework, one can automate collection of beam data and subsequently pull that data into a model which then outputs the necessary adjustments to beam optics. Tuning based on this method is model coupled accelerator tuning (MCAT) and includes pursuits like the training of machine learning (ML) agents to optimize corrections benders. A summary of the framework will be provided followed by a description of the different applications of the MCAT method - both those currently being pursued, and those envisioned for the future.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".