MétaCan
Menu
Back to cohort
Record W1606346158 · doi:10.1063/1.1893385

Charge State Breeding with an ECRIS for ISAC at TRIUMF

2005· article· en· W1606346158 on OpenAlexaff
F. Ames

Bibliographic record

VenueAIP conference proceedings · 2005
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsTRIUMF
Fundersnot available
KeywordsIon sourceIonBooster (rocketry)Ion beamAtomic physicsPhysicsNuclear physicsMaterials scienceNuclear engineeringChemistryPlasmaEngineering

Abstract

fetched live from OpenAlex

For the acceleration of radioactive ions the usable mass range is limited by the A/q acceptance of the first accelerator stage. Since an efficient primary ion source normally produces singly charged ions, charge state breeding is necessary if higher masses are to be accelerated. At TRIUMF an ECR source has been chosen as a breeder due to its potential high efficiency in producing intermediate A/q values. To minimize the necessity for further stripping an A/q around 6 is desirable. A 14 GHz “PHOENIX” booster from Pantechnik has been set up on a test bench. The singly charged ions are produced from different ion sources, which can be mounted in a standard ISAC target‐ion‐source set‐up. For the first tests an ECR source to produce noble gas beams has been chosen. The aim of the measurements at the test bench is to find the optimum operation conditions of the charge state booster and the injection and extraction ion optics. Working with radioactive ions always means that the system should aim for high efficiency, as the production of such species is limited. Therefore, special emphasis has to be put on the highest yield for the production of the desired charge state. A second point is the analysis of the extracted beam quality in order to optimize mass separation and transport efficiency. The paper shows the status of the set‐up and reports on first results of the charge breeding of Ar, Ne and Xe. With Xe a total efficiency of 22.5 % has been achieved.

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 score0.888
Threshold uncertainty score0.797

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 communication0.0000.001
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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueAIP conference proceedingsSame topicParticle accelerators and beam dynamicsFrench-language works237,207