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Status and Future Perspective of the TRIUMF E-Linac

2020· article· en· W3207139655 on OpenAlexaff
Stephanie Rädel, M. Alcorta, F. Ames, Eric Chapman, K. Fong, B.M. Humphries, O. Kester, David Kishi, Shane Koscielniak, Robert Laxdal, Yanyun Ma, Thomas Planche, Mike Rowe, V. Verzilov

Bibliographic record

VenueJACOW · 2020
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsTRIUMF
Fundersnot available
KeywordsPerspective (graphical)Linear particle acceleratorPhysicsComputer scienceOpticsBeam (structure)Artificial intelligence

Abstract

fetched live from OpenAlex

The currently installed configuration of TRIUMF’s superconducting electron linac (e-linac) can produce an electron beam up to 30MeV and 10mA. Low beam power commissioning of the segment spanning the electron gun to high energy dump took place in summer 2018 with an attained beam energy of 25MeV. As the driver of the ARIEL project, the e-linac will deliver electrons to a photo-converter target station for the production of neutron-rich rare isotope beams (RIB) via photo fission. The e-linac will have sufficient beam power to support the demands of other user community rare isotope beams. This driver accelerator could server as a production machine for high field THz radiation and as irradiation center. A recirculation of the beam would be beneficial for RIB production at higher beam energy and would allow for high bunch compression to generate THz radiation. Such a system would also allow for the investigation of a high beam intensity energy recovery linac. To this end, TRIUMF is investigating the design of such a recirculation and the beam dynamics as a first step.

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)0.004
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.004

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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