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Status and Issues (Microphonics, LFD, MPS) with TRIUMF ARIEL e-Linac Commissioning

2018· article· en· W3176722102 on OpenAlexaff
Shane Koscielniak, M. Alcorta, F. Ames, Eric Chapman, K. Fong, B.M. Humphries, O. Kester, David Kishi, Robert Laxdal, Yanyun Ma, Thomas Planche, Mike Rowe, Stephanie Rädel, V. Verzilov, Zhongyuan Yao

Bibliographic record

VenueJACOW · 2018
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsTRIUMF
Fundersnot available
KeywordsMicrophonicsLinear particle acceleratorNuclear engineeringNuclear physicsPhysicsEngineeringBeam (structure)

Abstract

fetched live from OpenAlex

The ARIEL electron linac (e-linac) is designed to generate cw beams of up to 30 MeV and 10 mA for delivery to a photo-convertor. Bremsstrahlung induced fission of a production target yields neutron-rich rare isotope beams to be supplied to the ISAC experimental facilities. The beam power will eventually reach 300 kW, and a machine protection system (MPS) with 10 μs rapidity is essential. The e-linac, which adopts 1.3 GHz, 2K SRF technology, is composed of a 10 MeV single-cavity injector cryomodule (EINJ) and a 20 MeV two-cavity accelerator cryomodule (EACA). The latter has vector-sum control of two cavities driven from a single klystron. Beam commissioning of these systems is ongoing since 2016. The magnetic optics and MPS commissioning to 10 MeV is reported herein. Beam has been accelerated up to 25 MeV, and thread-ed to the high energy dump (EHD). A campaign to investigate microphonics driving terms, LN2 disturb-ances, and a ponderomotive instability in the EACA, is underway.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.007

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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2018
Admission routes1
Has abstractyes

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