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Preface

2022· article· en· W4311346260 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsTimelineNeutrinoPhysics beyond the Standard ModelWIMPDark matterColliderParticle physicsNuclear physics

Abstract

fetched live from OpenAlex

These are the proceedings of the “New Scientific Opportunities at the TRIUMF ARIEL e-linac” workshop, which took place at TRIUMF laboratory, in Vancouver, Canada, May 25-27, 2022. The workshop was a hybrid of in-person and online attendance via Zoom, gathering together theorists and experimentalists with shared interests in MeV-scale physics at the intensity frontier, in order to stimulate ideas and collaboration for novel applications of new, high-intensity, modest-energy accelerators like the ARIEL e-Linac. While the LHC and its large detector collaborations continue to seek BSM physics at the energy frontier, and increasingly large subterranean detectors probe neutrino and dark matter signals with greater and greater sensitivities, the increasing scale of these experiments represents a substantial undertaking, in terms of cost and timeline. These have yielded important results, confirming the existence of the Higgs and furthering our understanding of neutrino dynamics, but they have not yet provided a clear view of what lies beyond the Standard Model. The prevailing expectations of the previous decades—minimal supersymmetry, WIMP-based dark matter—have not yet been observed. A complementary experimental thrust in the quest for new physics is to probe lower energy scales at the intensity and precision frontiers. Such experiments can be mounted more nimbly, and in parallel, to focus on different reported anomalies as lampposts, to improve precision in places where new physics may lurk, and to test new classes of BSM physics that may arise at those energies. With no definitive guidance on the form an underlying theory must take, this is an increasingly appealing approach to the search. Modern accelerator designs can provide higher and higher beam currents, achieving high luminosities without the need for thick targets, unlocking new experimental avenues. Energy Recovery Linacs (ERLs) at the sub-GeV scale are a particularly exciting emerging platform, with a series of machines at various levels of development, including the planned upgrade for ARIEL, the MESA accelerator complex currently under construction, and the planned PERLE facility. The articles in these proceedings showcase the experimental avenues now being explored, or which could be undertaken at ARIEL and similar-scale accelerators, as well as theoretical studies in these regimes, and the status of accelerators that will enable these and future experiments that aim to address current anomalies and outstanding questions in particle and nuclear physics. The workshop was funded by the Gordon and Betty Moore Foundation, to which the organizing committee give their sincere thanks. Jan Bernauer (CFNS, Stony Brook University and RIKEN BNL Research Center) Ross Corliss (CFNS, Stony Brook University) Michael Hasinoff (University of British Columbia) Rituparna Kanungo (Saint Mary’s University) Jeffery Martin (University of Winnipeg) Richard Milner (Massachusetts Institute of Technology) Katherine Pachal (TRIUMF) Stanley Yen (TRIUMF)

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)0.002
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.629
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0070.005
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.6290.492

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 head0.239
Teacher spread0.218 · 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".

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Citations0
Published2022
Admission routes1
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