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Record W3114157542 · doi:10.1139/cjp-2020-0299

Main parameters of SppC-based “linac-ring <i>eA</i>” and “ring-ring <i>µA</i>” colliders

2020· article· en· W3114157542 on OpenAlexvenueno aff
Bora Ketenoğlu

Bibliographic record

VenueCanadian Journal of Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsColliderLuminosityMuon colliderMuonLeptonNuclear physicsParticle physicsLinear particle acceleratorProtonRing (chemistry)International Linear ColliderStorage ringElectronBeam (structure)Particle acceleratorAstrophysicsOptics

Abstract

fetched live from OpenAlex

Concerning future lepton-nucleus colliders, International Linear Collider (ILC) and Plasma Wake Field Accelerator-Linear Collider (PWFA-LC) electrons in the order of 0.5 TeV and 5 TeV, respectively, colliding with Super proton–proton Collider (SppC)’s lead ions, are considered as “linac-ring eA” options. In addition, the 1.5 TeV option of the Muon Collider (MC) vs. 208Pb82+ ions of the SppC is also taken into account as a “ring-ring µA” collider. Luminosity values of the SppC-based eA and µA colliders are estimated. 3075 TeV lead parameters for the 100 km-circumference option of the SppC are taken into account to optimize luminosities of electron–nucleus and muon–nucleus collisions, keeping beam–beam effects and disruption in mind. It is shown that luminosities around 1030 cm−2 s−1 and 1032 cm−2 s−1 for eA and µA colliders, respectively, can be achieved by moderate upgrades of lepton and nucleus beam parameters.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.019
GPT teacher head0.225
Teacher spread0.206 · 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 designSimulation or modeling
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

Citations2
Published2020
Admission routes1
Has abstractyes

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