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Record W3136684496 · doi:10.18429/jacow-srf2017-moxa03

The 30MeV Stage of the ARIEL e-linac

2017· article· en· W3136684496 on OpenAlex
Robert Laxdal, Z. Ang, Thomas Au, K. Fong, O. Kester, Shane Koscielniak, Alexey Koveshnikov, Michael Laverty, Yanyun Ma, D. Storey, Edward Thoeng, Zhongyuan Yao, Qiwen Zheng, Vladimir Zvyagintsev

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJACOW · 2017
Typearticle
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsTRIUMF
Fundersnot available
KeywordsStage (stratigraphy)Linear particle acceleratorComputer sciencePhysicsGeologyOptics

Abstract

fetched live from OpenAlex

A MW class cw superconducting electron linac (e-Linac) is being installed at TRIUMF as a driver for radioactive beam production as part of the ARIEL project. The e-linac final configuration is planned to consist of five 1.3GHz nine-cell cavities housed in three cryomodules with one single cavity injector cryomodule (EINJ) and two double cavity accelerating cryomodules (EACA, EACB) to accelerate in continuous-wave (cw) up to 10mA of electrons to 50MeV. The e-Linac is being installed in stages. A demonstrator phase (2014) consisting of a 300kV electron gun, EINJ, and a partially outfitted EACA with just one accelerating cavity was installed for initial technical and beam tests to 22.9MeV. A Stage 2 upgrade now installed has a completed EACA to reach an operational goal of 3mA of electrons to 30MeV for first science from the ARIEL ISOL targets. A single 290kW klystron is used to feed the two EACA cavities in vector-sum closed-loop control. The paper is focused on the SRF challenges: systems design, cavity and cryomodule performance, rf ancillaries preparation and performance, LLRF and RF system performance and final beam test results.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.229

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 head0.226
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